Lattice Reduction Aided MMSE Tomlinson-Harashima Precoding Based on VBLAST Algorithm for MIMO Systems

A lattice reduction aided (LRA) minimum mean square error (MMSE) Tomlinson-Harashima pre-coding (THP) was proposed based on vertical Bell Labs layered space time (VBLAST) algorithm for multiple input multiple output (MIMO) systems. The extended channel was used to provide optimal tradeoff between residual interference and noise amplification. The processing based on lattice reduction method helps achieve maximal diversity order. The VBLAST algorithm was applied to get the optimal processing ordering for successive interference cancellation (SIC) at transmitter. Simulation results show that the proposed algorithm outperforms conventional THP and the LRA zero-forcing (ZF) THP with full diversity order.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Design and Implementation of Nonlinear Precoding for MIMO-SDMA Toward 6G Wireless

In this paper,we present a novel and robust nonlinear precoding(NLP)design and detection structure specifically tailored for multiple-input multipleoutput space division multiple access(MIMO-SDMA)systems toward 6G wireless.Our approach aims to effectively mitigate the impact of imperfect channel estimation by leveraging the channel fluctuation mean square error(MSE)for reconstructing a highly accurate precoding matrix at the transmitter.Furthermore,we introduce a simplified receiver structure that eliminates the need for equalization,resulting in reduced interference and notable enhancements in overall system performance.We conduct both computer simulations and experimental tests to validate the efficacy of our proposed approach.The results reveals that the proposed NLP scheme offers significant performance improvements,making it particularly well-suited for the forthcoming 6G wireless.

High Energy Efficiency Dynamic Connected Hybrid Precoding for mm Wave Massive MIMO Systems

This paper considers a high energy efficiency dynamic connected(HEDC)structure,which promotes the practicability and reduces the power consumption of hybrid precoding system by lowresolution phase shifters(PSs).Based on the proposed structure,a new hybrid precoding algorithm is presented to optimize the energy efficiency,namely,HP-HEDC algorithm.Firstly,via a new defined effective optimal precoding matrix,the problem of optimizing the analog switch precoding matrix is formulated as a sparse representation problem.Thus,the optimal analog switch precoding matrix can be readily obtained by the branch-and-bound method.Then,the digital precoding matrix optimization problem is modeled as a dictionary update problem and solved by the method of optimal direction(MOD).Finally,the diagonal entries of the analog PS precoding matrix are optimized by exhaustive search independently since PS and antenna is one-to-one.Simulation results show that the HEDC structure enjoys low power consumption and satisfactory spectral efficiency.The proposed algorithm presents at least 50%energy efficiency improvement compared with other algorithms when the PS resolution is set as 3-bit.

Hybrid Block Diagonalization Precoding for Multi-User Weighted Sum-Rate Maximization

This paper studies large-scale multi-input multi-output(MIMO)orthogonal frequency division multiplexing(OFDM)communications in a broadband frequency-selective channel,where a massive MIMO base station(BS)communicates with multiple users equipped with multi-antenna.We develop a hybrid precoding design to maximize the weighted sum-rate(WSR)of the users by optimizing the digital and the analog precoders alternately.For the digital part,we employ block-diagonalization to eliminate inter-user interference and apply water-filling power allocation to maximize the WSR.For the analog part,the optimization of the PSN is formulated as an unconstrained problem,which can be efficiently solved by a gradient descent method.Numerical results show that the proposed block-diagonal hybrid precoding algorithm can outperform the existing works.

Secure symbol level precoding for cell-free network based on band-region constraint of the eavesdropper’s receiving signal

A symbol level secure precoding scheme based on band-region constraint of the eavesdropper’s receiving signal is proposed to enhance the energy efficiency of cell-free multiple-input multiple-output(MIMO)networks in the presence of an eavesdropper while guaranteeing the quality of service(QoS)of user and the security of system.Moreover,to lighten its high computational complexity,original problem is divided into several cascade sub-problems firstly,and then those sub-problems are handled by combining Lagrangian dual function and improved Hooke-Jeeves method together.Comparative ex-periment with other secure symbol-level precoding schemes demonstrate that proposed scheme can achieve the lower power consumption with almost same symbol error rate and QoS of user.

A Precoding Scheme to Reduce the Effect of Channel Correlation for MIMO-PLC System

MIMO technique can provide higher information throughput and transmission reliability for the PLC system.However,the MIMO-PLC system based on three-conductor cable has a high correlation among its sub-channels.Spatial multiplexing technology will be affected by the spatial correlation between MIMO-PLC sub-channels.To reduce the system bit error rate caused by MIMO-PLC correlation among sub-channels,this paper proposed a phase rotation precoding scheme for the 2×2 closed-loop MIMO-PLC system.According to the channel transfer function of high correlation MIMO-PLC system,the phase rotation precoding matrix F is calculated,and the transmission signal matrix S is modulated with the F,the code distance at the receiving point with smallest code distance is increased by phase rotation.Simulation results show that the scheme can effectively reduce the bit error rate of the 2×2 MIMO-PLC system based on ML detection,and significantly improve the system performance.

Evaluation of Linear Precoding Schemes for Cooperative Multi-Cell MU MIMO in Future Mobile Communication Systems

In Mobile Communication Systems, inter-cell interference becomes one of the challenges that degrade the system’s performance, especially in the region with massive mobile users. The linear precoding schemes were proposed to mitigate interferences between the base stations (inter-cell). These schemes are categorized into linear and non-linear;this study focused on linear precoding schemes, which are grounded into three types, namely Zero Forcing (ZF), Block Diagonalization (BD), and Signal Leakage Noise Ratio (SLNR). The study included the Cooperative Multi-cell Multi Input Multi Output (MIMO) System, whereby each Base Station serves more than one mobile station and all Base Stations on the system are assisted by each other by shared the Channel State Information (CSI). Based on the Multi-Cell Multiuser MIMO system, each Base Station on the cell is intended to maximize the data transmission rate by its mobile users by increasing the Signal Interference to Noise Ratio after the interference has been mitigated due to the usefully of linear precoding schemes on the transmitter. Moreover, these schemes used different approaches to mitigate interference. This study mainly concentrates on evaluating the performance of these schemes through the channel distribution models such as Ray-leigh and Rician included in the presence of noise errors. The results show that the SLNR scheme outperforms ZF and BD schemes overall scenario. This implied that when the value of SNR increased the performance of SLNR increased by 21.4% and 45.7% for ZF and BD respectively.

Low Complexity Joint Hybrid Precoding for Millimeter Wave MIMO Systems

In millimeter wave(mmWave) multiple-input multiple-output(MIMO) systems, hybrid precoding has been widely used to overcome the severe propagation loss. In order to improve the spectrum efficiency with low complexity, we propose a joint hybrid precoding algorithm for single-user mmWave MIMO systems in this paper. By using the concept of equivalent channel, the proposed algorithm skillfully utilizes the idea of alternating optimization to complete the design of RF precoder and combiner. Then, the baseband precoder and combiner are computed by calculating the singular value decomposition of the equivalent channel. Simulation results demonstrate that the proposed algorithm can achieve satisfactory performance with quite low complexity. Moreover, we investigate the effects of quantization on the analog components and find that the proposed scheme is effective even with coarse quantization.

Hybrid Precoding for mm Wave Massive MIMO Systems with Different Antenna Arrays

In this paper, the performance of hybrid precoding is investigated for mmWave massive MIMO systems with different antenna arrays. The hybrid precoding with partially connected architecture (PCA) is adopted. The spectral efficiency (SE) and received energy efficiency (EE) are investigated by considering four types of antenna arrays, including uniform linear array (ULA), uniform rectangular planar array (URPA), uniform hexagonal planar array (UHPA), and uniform circular planar array (UCPA), respectively. We focus on analysis at the antenna response vector and utilize the idea of orthogonal matching pursuit algorithm to seek the optimal hybrid precoder. Furthermore, the trade-off of precoding architectures is studied between SE and received EE. Simulation results show that if the uniform planar array antenna is more concentrated, the SE and receive EE will be higher. Considering SE and received EE, the performance of planar arrays outperform linear array. There exist different optimal radio-frequency chain numbers to maximize the SE for planar array and linear array. In addition, the PCA can achieve relatively higher received EE while the SE is close to the fully connected architecture and the full digital architecture.

Fairness-Oriented Hybrid Precoding for Massive MIMO Maritime Downlink Systems with Large-Scale CSIT

Different from conventional cellular networks, a maritime communication base station(BS) has to cover a much wider area due to the limitation of available BS sites. Accordingly the performance of users far away from the BS is poor in general. This renders the fairness among users a challenging issue for maritime communications. In this paper, we consider a practical massive MIMO maritime BS with hybrid digital and analog precoding. Only the large-scale channel state information at the transmitter(CSIT) is considered so as to reduce the implementation complexity and overhead of the system. On this basis, we address the problem of fairness-oriented precoding design. A max-min optimization problem is formulated and solved in an iterative way. Simulation results demonstrate that the proposed scheme performs much better than conventional hybrid precoding algorithms in terms of minimum achievable rate of all the users, for the typical three-ray maritime channel model.

Quantized Hybrid Precoding Design for Millimeter-Wave Large-Scale MIMO Systems

Millimeter wave(mmWave) and large-scale multiple input multiple output(MIMO) are two emerging technologies in fifth-generation wireless communication systems. The power consumption and hardware cost of radio frequency(RF) chains increase exponentially with the bit resolution of analog-to-digital converters(ADCs) and digital-to-analog converters(DACs). One promising solution is to employ few RF chains with low-bit ADCs and DACs. In this paper, we consider mmWave large-scale MIMO systems with low bits DACs and ADCs. Leveraging on the Bussgang theorem and the additive quantization noise model(AQNM), a closed-form expression of the achievable rate is derived to show the effect of the ADCs? and DACs? resolution. Moreover, an orthogonal matching pursuit(OMP) based hybrid precoding algorithm is proposed to increase the achievable rate. Our results show that the impact of DACs is more pronounced than the impact of ADCs. Furthermore, 5-bit ADCs and DACs are sufficient at the transceiver to operate without a significant performance loss.

Geometric Mean Decomposition Based Hybrid Precoding for Millimeter-Wave Massive MIMO

Hybrid precoding can reduce the number of required radio frequency(RF)chains in millimeter-Wave(mmWave) massive MIMO systems. However, existing hybrid precoding based on singular value decomposition(SVD) requires the complicated bit allocation to match the different signal-to-noise-ratios(SNRs) of different sub-channels. In this paper,we propose a geometric mean decomposition(GMD)-based hybrid precoding to avoid the complicated bit allocation. Specifically,we seek a pair of analog and digital precoders sufficiently close to the unconstrained fully digital GMD precoder. To achieve this, we fix the analog precoder to design the digital precoder, and vice versa. The analog precoder is designed based on the orthogonal matching pursuit(OMP) algorithm, while GMD is used to obtain the digital precoder. Simulations show that the proposed GMD-based hybrid precoding achieves better performance than the conventional SVD-based hybrid precoding with only a slight increase in complexity.

Precoding Based Channel Prediction for Underwater Acoustic OFDM

The life duration of underwater cooperative network has been the hot topic in recent years. And the problem of node energy consuming is the key technology to maintain the energy balance among all nodes. To ensure energy efficiency of some special nodes and obtain a longer lifetime of the underwater cooperative network, this paper focuses on adopting precoding strategy to preprocess the signal at the transmitter and simplify the receiver structure. Meanwhile, it takes into account the presence of Doppler shifts and long feedback transmission delay in an underwater acoustic communication system. Precoding technique is applied based on channel prediction to realize energy saving and improve system performance. Different precoding methods are compared. Simulated results and experimental results show that the proposed scheme has a better performance, and it can provide a simple receiver and realize energy saving for some special nodes in a cooperative communication.

A New TDD Scheme and Interference-Aware Precoding for Device-to-Device Underlay Massive MIMO

In device-to-device(D2D)underlay cellular networks with downlink spectrum sharing,massive MIMO seems promising as the large number of antennas at the base station(BS) can be utilized to suppress interference.However,the channel state information(CSI) from the BS to D2D receivers is required to obtain this advantage.In this paper,we first propose a novel time division duplex(TDD) scheme for D2D users to acquire this CSI,without additional pilot overhead.Moreover,we propose an interference-aware MMSE precoder utilizing the acquired CSI from the BS to not only cellular users but also D2D users to suppress the cellular-to-D2D interference.Simulation results show that our proposed TDD scheme and precoder can significantly improve the achievable sum spectral efficiency(SE) and D2D SE,compared to the classical MMSE precoder.Compared with the interferenceaware ZF precoder,whose performance severely degrades for large user numbers,our proposed interference-aware MMSE precoder can always guarantees a high and stable performance in terms of achievable SE.

Joint Source and Relay Precoding/Beamforming Algorithm for OFDM Multi-user Cooperative Relay Systems

In order to reduce the feedback load of multi-user orthogonal frequency division multiplexing （ OFDM ） -based wireless systems, a practiral limited bits feedback precoding algorithm is proposed with direct source-destination link based on amplify-and- forward cooperative relay network under frequency selective fading channels. Using joint minimum mean square error（MMSE） filter, the receiving decoding matrix is designed for each user in the paper. Source precoding （beamforming） matrix is optimized with convex function of weight mean square error （MSE）. Relay precoding matrix is obtained under MSE decomposition and convex optimization. The precoding matrix index is fed back for clustered subcarrier of OFDM with limited feedback. Then using interpolation algorithm, all precoding matrices are achieved at base station （BS） and relay nodes. Simulations indicate the effectiveness of the proposed limited feedback joint precoding and beam_formlng design. The proposed method can improve bit error rate （BER） performance and obtain better sum-rate performance in contrast to existing algorithms. It displays the BER performance is close to that of the unquantified precoding feedback method.

Lattice-reduction-aided MMSE precoding for correlated MIMO channels and performance analysis

The lattice-reduction （LR） has been developed to im- prove the performance of the zero-forcing （ZF） precoder in multiple input multiple output （MIMO） systems. Under the assumptions of uncorrelated flat fading channel model and perfect channel state information at the transmitter （CSIT）, an LR-aided ZF precoder is able to collect the full transmit diversity. With the complex Lenstra- Lenstra-Lov^sz （LLL） algorithm and limited feedforward structure, an LR-aided linear minimum-mean-square-error （LMMSE） pre- coder for spatial correlated MIMO channels and imperfect CSIT is proposed to achieve lower bit error rate （BER）. Assuming a time division duplexing （TDD） MIMO system, correlated block flat fad- ing channel and LMMSE uplink channel estimator, it is proved that the proposed LR-aided LMMSE precoder can also obtain the full transmit diversity through an analytical approach. Furthermore, the simulation results show that with the quadrature phase shift keying （QPSK） modulation at the transmitter, the uncoded and coded BERs of the LR-aided LMMSE precoder are lower than that of the traditional LMMSE precoder respectively when Eb-No is greater than 10 dB and 12 dB at all correlation coefficients.

Multi-Task Deep Learning Based Hybrid Precoding for mmWave Massive MIMO System

Due to the different signal-to-noise ratio(SNR)of each subchannel,the bit error rate(BER)of hybrid precoding based on singular value decomposition(SVD)decreases.In this paper,we propose a multi-task learning based precoding network(PN)model to solve the BER loss problem caused by SVD based hybrid precoding under imperfect channel state information(CSI).Specifically,we firstly generate a dataset including imcomplete CSI input channel matrix and corresponding output labels to train the PN model.The output labels are designed based on uniform channel decomposition(UCD)which decomposes the channel into multiple subchannels with same gain,while the vertical-bell layered space-time structure(V-BLAST)signal processing technology is combined to eliminate the inner interference of the subchannels.Then,the PN model is trained to design the analog and digital precoding/combining matrix simultaneous.Simulation results show that the proposed scheme has only negligible gap in spectrum efficiency compared with the fully digital precoding,while achieves better BER performance than SVD based hybrid precoding.

Approximate Iteration Detection and Precoding in Massive MIMO

Massive multiple-input multiple-output provides improved energy efficiency and spectral efficiency in 5 G. However it requires large-scale matrix computation with tremendous complexity, especially for data detection and precoding. Recently, many detection and precoding methods were proposed using approximate iteration methods, which meet the demand of precision with low complexity. In this paper, we compare these approximate iteration methods in precision and complexity, and then improve these methods with iteration refinement at the cost of little complexity and no extra hardware resource. By derivation, our proposal is a combination of three approximate iteration methods in essence and provides remarkable precision improvement on desired vectors. The results show that our proposal provides 27%-83% normalized mean-squared error improvement of the detection symbol vector and precoding symbol vector. Moreover, we find the bit-error rate is mainly controlled by soft-input soft-output Viterbi decoding when using approximate iteration methods. Further, only considering the effect on soft-input soft-output Viterbi decoding, the simulation results show that using a rough estimation for the filter matrix of minimum mean square error detection to calculating log-likelihood ratio could provideenough good bit-error rate performance, especially when the ratio of base station antennas number and the users number is not too large.

Blind CFO estimation algorithm for OFDM systems by using generalized precoding and trilinear model

This paper discusses the blind carrier frequency offset （CFO） estimation for orthogonal frequency division multiplexing （OFDM） systems by utilizing trilinear decomposition and genera- lized preceding. Firstly, the generalized precoding is employed to obtain multiple covariance matrices which are requisite for the trilinear model, and then a novel CFO estimation algorithm is proposed for the OFDM system. Compared with both the joint diagonalizer and estimation of signal parameters via rotational invariant technique （ESPRIT）, the proposed algorithm enjoys a better CFO estimation performance. Furthermore, the proposed algorithm can work well without virtual carriers. Simulation results illustrate the performance of this algorithm,