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.

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.

Hybrid Precoding for Cluster-Based Multi-Carrier Beam Division Multiple Access in Terahertz Wireless Communications

Terahertz(THz)wireless communication has the capability to connect massive devices using its ultra-large spectrum resource.We propose a hybrid precoding scheme for the cluster-based multi-carrier beam division multiple access(MC-BDMA)to enable THz massive connections.Both the inter-beam interference and inter-band power leakage in this system are considered.A mathematical model is established to analyze and reduce their effects on the THz signal transmission.By considering the peculiarities of THz channels and characteristics of THz hardware components,we further propose a three-step hybrid precoding algorithm with low complexity,where the received signal power enhancement,the inter-beam interference elimination,and the inter-band power leakage suppression are conducted in succession.Simulation results are presented to demonstrate the high spectrum efficiency and high energy efficiency of our proposed algorithm,especially in the massive-connection scenarios.

Optimal Hybrid Precoding Based QoE for Partially Structured Massive MIMO System

Precoding is a beamforming technique that supports multi-streamtransmission in which the RF chain plays a significant role as a digital precoding at the receiver for wireless communication. The traditional precodingcontains only digital signal processing and each antenna connects to each RFchain, which provides high transmission efficiency but high cost and hardwarecomplexity. Hybrid precoding is one of the most popular massive multipleinput multiple output (MIMO) techniques that can save costs and avoid usingcomplex hardware. At present, network services are currently in focus with awide range of traffic volumes. In terms of the Quality of Service (QoS), it iscritical that service providers pay a lot of attention to this parameter and itsrelationship to Quality of Experience (QoE) which is the measurement of theoverall level of user satisfaction. Therefore, this paper proposes hybrid precoding of a partially structured system to improve transmission efficiency andallocate resources to provide network services to users for increasing the usersatisfaction under power constraints that optimize the quality of basebandprecoding and radio frequency (RF) precoding by minimizing alternatingalgorithms. We focus on the web browsing, video, and Voice over IP (VOIP)services. Also, a Mean Opinion Score (MOS) is employed to measure thelevel of user satisfaction. The results show that the partially structured systemprovides a good user satisfaction with the network’s services. The partiallystructured system provides high energy efficiency up to 85%. Considering webservice, the partially structured system for 10 users provides MOS at 3.21 whichis higher than 1.75 of fully structured system.

Performance Analysis of 5 G Wireless Hybrid Precoding Using Evolutionary Algorithms

Emerging 5G communication solutions utilize the millimeter wave(mmWave)band to alleviate the spectrum deficit.In the mmWave range,Multiple Input Multiple Output(MIMO)technologies support a large number of simultaneous users.In mmWave MIMO wireless systems,hybrid analog/digital precoding topologies provide a reduced complexity substitute for digital precoding.Bit Error Rate(BER)and Spectral efficiency performances can be improved by hybrid Minimum Mean Square Error(MMSE)precoding,but the computation involves matrix inversion process.The number of antennas at the broadcasting and receiving ends is quite large for mm-wave MIMO systems,thus computing the inverse of a matrix of such high dimension may not be practically feasible.Due to the need for matrix inversion and known candidate matrices,the classic Orthogonal Matching Pursuit(OMP)approach will be more complicated.The novelty of research presented in this manuscript is to create a hybrid precoder for mmWave communication systems using metaheuristic algorithms that do not require matrix inversion processing.The metaheuristic approach has not employed much in the formulation of a precoder in wireless systems.Five distinct evolutionary algorithms,such as Harris–Hawks Optimization(HHO),Runge–Kutta Optimization(RUN),Slime Mould Algorithm(SMA),Hunger Game Search(HGS)Algorithm and Aquila Optimizer(AO)are considered to design optimal hybrid precoder for downlink transmission and their performances are tested under similar practical conditions.According to simulation studies,the RUN-based precoder performs better than the conventional algorithms and other nature-inspired algorithms based precoding in terms of spectral efficiency and BER.

Reconfigurable intelligent surface based hybrid precoding for THz communications

Benefiting from the growth of the bandwidth,Terahertz(THz)communication can support the new application with explosive requirements of the ultra-high-speed rates for future 6G wireless systems.In order to compensate for the path loss of high frequency,massive Multiple-Input Multiple-Output(MIMO)can be utilized for high array gains by beamforming.However,the existing THz communication with massive MIMO has remarkably high energy consumption because a large number of analog phase shifters should be used to realize the analog beamforming.To solve this problem,a Reconfigurable Intelligent Surface(RIS)based hybrid precoding architecture for THz communication is developed in this paper,where the energy-hungry phased array is replaced by the energy-efficient RIS to realize the analog beamforming of the hybrid precoding.Then,based on the proposed RIS-based architecture,a sum-rate maximization problem for hybrid precoding is investigated.Since the phase shifts implemented by RIS in practice are often discrete,this sum-rate maximization problem with a non-convex constraint is challenging.Next,the sum-rate maximization problem is reformulated as a parallel Deep Neural Network(DNN)based classification problem,which can be solved by the proposed low-complexity Deep Learning based Multiple Discrete Classification(DL-MDC)hybrid precoding scheme.Finally,we provide numerous simulation results to show that the proposed DL-MDC scheme works well both in the theoretical Saleh-Valenzuela channel model and practical 3GPP channel model.Compared with existing iterative search algorithms,the proposed DL-MDC scheme significantly reduces the runtime with a negligible performance loss.

Hybrid Precoding Design Achieving Fully Digital Performance for Millimeter Wave Communications

As a promising candidate for millimeter wave(mmWave)multiple-input and multiple-output(MIMO)communications,hybrid precoding techniques can reap the benefit of large antenna arrays,yet with only limited number of radio frequency(RF)chains.In this paper,we investigate the problem of achieving the same performance of the fully digital system with hybrid precoding.Specifically,for the single user MIMO system,we propose a closed form hybrid precoding design that can achieve the optimal fully digital performance for both frequency-flat and frequency-selective channels,and only requires the number of RF chains to equal the number of paths of the channel.The design for the case with even less RF chains is also given.Furthermore,for the multiuser(MU)system with single antenna at each mobile terminal(MT),twoMU beamforming schemes are considered,which are the directional beamforming and zero-forcing.We show that for both schemes,the fully digital performance can be achieved with our proposed hybrid precoding designs with the number of RF chains no less than the sum number of channel paths from the base station to all the selected MTs.Numerical results are provided to validate our analytical results and show the performance gain of the proposed hybrid precoding designs compared to other benchmark schemes.

Improved Hybrid Beamforming for mmWave Multi-User Massive MIMO

Massive multiple input multiple output(MIMO)has become essential for the increase of capacity as the millimeter-wave(mmWave)communication is considered.Also,hybrid beamforming systems have been studied since full-digital beamforming is impractical due to high cost and power consumption of the radio frequency(RF)chains.This paper proposes a hybrid beamforming scheme to improve the spectral efciency for multi-user MIMO(MU-MIMO)systems.In a frequency selective fading environment,hybrid beamforming schemes suffer from performance degradation since the analog precoder performs the same precoding for all subcarriers.To mitigate performance degradation,this paper uses the average channel covariance matrix for all subcarriers and considers an iterative algorithm to design analog precoder using approximation techniques.The analog precoder is iteratively updated for each column until it converges.The proposed scheme can reduce errors in the approximating process of the overall spectral efciency.This scheme can be applied to fully-connected and partially-connected structures.The simulation results show that spectral efciency performance for the proposed scheme is better than the conventional schemes.The proposed scheme can achieve similar performance with full-digital beamforming by using a sufciently large number of RF chains.Also,this paper shows that the proposed scheme outperforms other schemes in the frequency selective fading environment.This performance improvement can be achieved in both structures.

Wideband Channel Estimation for THz Massive MIMO

Terahertz(THz)communication is considered to be a promising technology for future 6G network.To overcome the severe attenuation and relieve the high power consumption,massive multipleinput multiple-output(MIMO)with hybrid precoding has been widely considered for THz communication.However,accurate wideband channel estimation,which is essential for hybrid precoding,is challenging in THz massive MIMO systems.The existing wideband channel estimation schemes based on the ideal assumption of common sparse channel support will suffer from a severe performance loss due to the beam split effect.In this paper,we propose a beam split pattern detection based channel estimation scheme to realize reliable wideband channel estimation in THz massive MIMO systems.Specifically,a comprehensive analysis on the angle-domain sparse structure of the wideband channel is provided by considering the beam split effect.Based on the analysis,we define a series of index sets called as beam split patterns,which are proved to have a one-to-one match to different physical channel directions.Inspired by this one-to-one match,we propose to estimate the physical channel direction by exploiting beam split patterns at first.Then,the sparse channel supports at different subcarriers can be obtained by utilizing a support detection window.This support detection window is generated by expanding the beam split pattern which is determined by the obtained physical channel direction.The above estimation procedure will be repeated path by path until all path components are estimated.Finally,the wideband channel can be recovered by calculating the elements on the total sparse channel support at all subcarriers.The proposed scheme exploits the wideband channel property implied by the beam split effect,i.e.,beam split pattern,which can significantly improve the channel estimation accuracy.Simulation results show that the proposed scheme is able to achieve higher accuracy than existing schemes.

Doubling Phase Shifters for Efficient Hybrid Precoder Design in Millimeter-Wave Communication Systems

Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter-wave(mmWave)communications,but its precoder design is highly complicated.In this paper,we propose a new hybrid precoder implementation,namely the double phase shifter(DPS)implementation,which enables highly tractable hybrid precoder design.Efficient algorithms are then developed for two popular hybrid precoder structures,i.e.,the fully-and partially-connected structures.For the fully-connected one,the RF-only precoding and hybrid precoding problems are formulated as a least absolute shrinkage and selection operator problem and a low-rank matrix approximation problem,respectively.In this way,computationally efficient algorithms are provided to approach the performance of the fully digital one with a small number of radio frequency(RF)chains.On the other hand,the hybrid precoder design in the partially-connected structure is identified as an eigenvalue problem.To enhance the performance of this cost-effective structure,dynamic mapping from RF chains to antennas is further proposed,for which a greedy algorithm and a modified K-means algorithm are developed.Simulation results demonstrate the performance gains of the proposed hybrid precoding algorithms over existing ones.It shows that,with the proposed DPS implementation,the fully-connected structure enjoys both satisfactory performance and low design complexity while the partially-connected one serves as an economic solution with low hardware complexity.

Performance evaluation of DHRR-RIS based HP design using machine learning algorithms

Reconfigurable Intelligent Surfaces(RIS)have emerged as a promising technology for improving the reliability of massive MIMO communication networks.However,conventional RIS suffer from poor Spectral Efficiency(SE)and high energy consumption,leading to complex Hybrid Precoding(HP)designs.To address these issues,we propose a new low-complexity HP model,named Dynamic Hybrid Relay Reflecting RIS based Hybrid Precoding(DHRR-RIS-HP).Our approach combines active and passive elements to cancel out the downsides of both conventional designs.We first design a DHRR-RIS and optimize the pilot and Channel State Information(CSI)estimation using an adaptive threshold method and Adaptive Back Propagation Neural Network(ABPNN)algorithm,respectively,to reduce the Bit Error Rate(BER)and energy consumption.To optimize the data stream,we cluster them into private and public streams using Enhanced Fuzzy C-Means(EFCM)algorithm,and schedule them based on priority and emergency level.To maximize the sum rate and SE,we perform digital precoder optimization at the Base Station(BS)side using Deep Deterministic Policy Gradient(DDPG)algorithm and analog precoder optimization at the DHRR-RIS using Fire Hawk Optimization(FHO)algorithm.We implement our proposed work using MATLAB R2020a and compare it with existing works using several validation metrics.Our results show that our proposed work outperforms existing works in terms of SE,Weighted Sum Rate(WSR),and BER.