Analog Precoding Research Articles

Performance analysis of massive MIMO offshore system with distributed antenna subarrays

In this paper, we propose a transmission scheme of analog precoding for an offshore communication system with distributed antenna subarrays to serve multiple vessels. The proposed analog precoding scheme relies only on line-of-sight (LOS) components of the fading channels, since the accurate estimation of small-scale fading information is challenging due to the varying sea state of maritime wireless channels, such as reflection, scattering, and ducting effect. We derive an approximate expression for the ergodic achievable rate, and emphasize the multiplexing gain analysis of the whole system with a large antenna configuration. In addition, we derive the probability density function (PDF) of signal-to-interference-and-noise ratio (SINR) and further obtain closed-form expressions of symbol error rate (SER). Monte Carlo simulations verify the theoretical analysis, and numerical results demonstrate that the performance of the proposed LOS-based analog precoding scheme for the distributed multiple-input multiple-output (MIMO) offshore system is extremely near to the limitation of system performance, i.e., the ideal situation without any interference.

Alternating Optimization Algorithms for Hybrid beamforming in mm wave MIMO systems

Millimeter wave (mmWave) interchanges has been viewed as a critical empowering innovation for 5G organizations, as it offers significant degrees more noteworthy range than current cell groups. As opposed to traditional various info numerous output (MIMO) frameworks, precoding in mmWave MIMO unable to performed completely at baseband utilizing computerized precoders, as just a predetermined no. of sign blenders and Analog to-digital converters (ADCs) can be upheld thinking about their expense and power utilization. Hybrid precoding transceiver design, joining a digital precoder and analog precoder, has as of late gotten impressive consideration. Nonetheless, the ideal plan of such half breed precoders has not been completely perceived. In this paper, treating the hybrid precoder plan as a lattice factorization issue, alternating minimization (AltMin) calculations will be proposed for two different half and half precoding structures, i.e., the completely associated what's more, somewhat associated structures. Specifically, for the fully connected structure, an AltMin calculation in view of complex advancement is proposed to move toward the exhibition of the completely computerized precoder, which, notwithstanding, has a high intricacy. Subsequently, a low-intricacy AltMin calculation is then proposed, by implementing a symmetrical limitation on the computerized precoder. Besides, for the to some degree associated structure, an AltMin calculation. Be that as it may, the ideal plan of is additionally evolved with the assistance of semidefinite unwinding. For commonsense execution, the proposed AltMin algorithms are additionally stretched out to the broadband setting with symmetrical recurrence division multiplexing (OFDM) adjustment. Recreation results will show critical execution gains of the proposed AltMin calculations over existing half breed precoding calculations. In addition, in light of the proposed calculations, recreation correlations between the two mixture precoding designs will give significant plan experiences.

Low-complexity hybrid precoding for sub-connected millimeter wave massive MIMO systems

Hybrid analog-digital precoding architectures facilitate practical implementation of millimeter wave (mmW) massive multiple-input multiple-output (mmW-mMIMO) systems by reducing the number of radio frequency chains required. The spectral efficiency (SE) and energy efficiency (EE) optimization problems of these systems are typically non-convex and NP-hard, as the joint optimization that involves the analog and digital precoding functions along with the constant modulus constraints required by analog phase shifters, results in highly non-linear problems. To address such problems, we propose a decoupled two-stage design. More specifically, in the first stage, a closed-form approximation of the effective channel is proposed, and thus the SE maximization problem can be transformed to an effective channel gain maximization problem. Then, the analog precoding matrices can be generated to facilitate the second stage, where the digital precoding matrix is devised for maximizing the system’s EE. In addition, the circuit power consumption statistics of the system modules are exploited and a sub-connected mmW-mMIMO architecture is adopted during EE optimization. Finally, simulation results are provided to validate the effectiveness of our proposed mmW-mMIMO scheme with hybrid precoding.

Performance analysis of insertion loss incorporated hybrid precoding for massive MIMO

<abstract> <p>Due to an increase in the number of users and a high demand for high data rates, researchers have resorted to boosting the capacity and spectral efficiency of the next-generation wireless communication. With a limited RF chain, hybrid analog digital precoding is an appealing alternative. The hybrid precoding approach divides the beamforming process into an analog beamforming network and a digital beamforming network of a reduced size. As a result, numerous hybrid beamforming networks have been proposed. The practical effects of signal processing in the RF domain, such as the additional power loss incurred by an analog beamforming network, were not taken into account. The effectiveness of hybrid precoding structures for massive MIMO systems was examined in this study. In particular, a viable hardware network realization with insertion loss was developed. Investigating the spectral and energy efficiency of two popular hybrid precoding structures, the fully connected structure, and the subconnected structure, it was found that in a massive MIMO, the subconnected structure always performed better than the fully connected structure. Characterizing the effect of quantized analog precoding, it was shown that the subconnected structure was able to achieve better performance with fewer feedback bits than the fully connected structure.</p> </abstract>

Cell-free ISAC massive MIMO systems with capacity-constrained fronthaul links

Integrated sensing and communication (ISAC), millimeter wave (mmWave) massive multiple-input multiple-output (MIMO), and cell-free networks are emerged as some of the most potential physical layer enablers for the future wireless communication networks. In this paper, we consider a cell-free ISAC mmWave massive MIMO system with hybrid analog and digital precoders while accounting for the impacts of capacity-constrained fronthaul links. In the system, the digital baseband processing is dictated at a geographical scale while the analog precoders are carried out at each transmit access point (AP). Based on the characteristics of the considered model, we formulate the optimization problem as a function of power allocation and fronthaul compression strategies. To address this problem, two block coordinate descend (BCD)-based methods are proposed to alternatively solve the three decomposed subproblems corresponding to the power allocation, the downlink and backward fronthaul compression strategies. Simulation results indicate that the proposed schemes can achieve 90% performance of the optimal full-search (FS) method with much lower computational complexity. In addition, there exists an optimal configuration of APs, UEs, and antennas for a cell-free ISAC system with capacity-constrained fronthaul links.

Hybrid distributed online and alternating convex optimization‐based initial value accelerated hybrid precoding for millimeter wave multiple‐input‐multiple‐output

SummaryThe hybrid precoding problem is considered a Frobenius norm reduction problem for the narrowband channel in a millimeter wave (mmWave) with multiple inputs and outputs (mmWave MIMO). This work proposes a hybrid distributed online and alternating convex optimization (HDO‐ACO) algorithm to improve hybrid precoding (HP), interpreted as a trace minimization problem. HDO‐ACO alternately determines the digital and analog precoders to reduce the trace by keeping the other constant. Initially, HDO‐ACO uses Lagrange's method to determine the digital precoding subproblem. Then, it uses the integrated distributed online convex optimization and alternating minimization algorithm in the analog precoder design. In the HP method, the digital and analog precoders are iteratively updated until the highest number of iterations or convergence is reached. But this hybrid precoding method requires an initial analog precoding matrix input to begin the iteration. The algorithms converge gradually and fall into a suboptimal solution when the initial analog precoding matrix is set randomly. Hence, an initial value acceleration‐based heuristic approach is used in the HDO‐ACO alternating minimization algorithm that calculates the initial feasible value of an alternating minimization method using channel conditions. The simulation results of the proposed HDO‐ACO algorithm are presented under bit error rate (BER), spectral efficiency (SE), and convergence behavior by comparing it with modified block coordinate descent–HP (MBCD‐HP), manifold optimization–alternating minimization (MO‐AltMin), and semi‐definite relaxation‐based alternating optimization (SDR‐AO). The proposed HDO‐ACO attains maximum SE and less BER than other hyper‐precoding designs.

An Overlapped Sub-array Hybrid Beamforming Architecture with Successive Interference Cancellation and Water-filling in Massive MIMO Systems

Over the years, wireless communication has significantly improved the data rate of mobile users. The key drive behind this success is the technological advancement in wireless communication. In the physical layer, hybrid beamforming has revolutionized the way signal reaches the user by constructively adding the signal at the destination thus improving the performance. Many researchers works have proposed different algorithms to solve for optimal hybrid beamforming. However, there is no single algorithm that can achieve both high spectral efficiency and energy efficiency at the same time. This work proposes a hybrid algorithm that combines successive interference cancellation and water filling and applies this algorithm in overlapped sub-array architecture (OSA). The former can successfully optimize for analog precoding in a subarray environment as it eliminates the interference between the successive sub-arrays. While the latter can allocate the power in each data stream proportionally, thus improving the spectral efficiency. The simulation results show that the proposed algorithm with OSA can achieve a near optimal performance in comparison to fully connected hybrid beamforming (FCH) and significantly larger performance in comparison to partially connected hybrid beamforming (PCH). Moreover, the proposed algorithm achieves 89.2% energy efficiency in comparison to PCH architecture. These results show that an OSA system with the proposed algorithm provides a better tradeoff between achieved spectral efficiency (SE) and energy efficiency (EE).

DL-PC algorithm for partially-connected hybrid precoding in massive MIMO systems

In hybrid precoding, a partially-connected (PC) architecture has aroused significant attention for its low hardware complexity. Unfortunately, the hybrid precoding with the PC architecture may suffer from a severe spectral efficiency loss. To overcome the loss, a deep learning-based hybrid precoding algorithm for the PC architecture is proposed in this paper. Firstly, the optimization problem of hybrid precoding is transformed into a neural network prediction problem. Furthermore, a convolutional neural network(CNN) is designed to predict the hybrid precoder, where the channel matrix is used as input. Especially, to meet the constraints of the hybrid precoder, two lambda layers are introduced into the CNN architecture. Specifically, a lambda1 layer is constructed to address the constant modulus constraint of the analog precoding matrix and the block diagonal constraint. Then, a lambda2 layer is constructed to optimize the digital precoding matrix and address the transmit power constraint. Finally, the vectorized hybrid precoding matrix is output by the well-trained network. Simulation results show that the proposed algorithm can enhance spectral efficiency and energy efficiency with lower computation time.

Two-Timescale-Based Beam Training for RIS-Aided Millimeter-Wave Multi-User MISO Systems

Reconfigurable intelligent surfaces (RISs), as a promising technology for 6 G communications, have received considerable attention. Herein, we investigate the downlink precoding in the RIS-aided millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) system, where the access point (AP) uses hybrid digital and analog precoders. Due to the constraints of practical hardware and the difficulty of acquiring channel state information, we consider codebook-based passive beamforming and analog beamforming, and then formulate the downlink precoding problem as a mixed-integer nonlinear programming (MINLP) problem. To solve this NP-hard MINLP problem with low channel estimation overheads, we investigate beam training for the codebook-based passive beamforming and analog beamforming, and the MINLP problem is then reduced to a traditional MU-MISO precoding problem. To further reduce the beam training overheads, we propose a two-timescale-based beam training (TT-BT) algorithm. We prove that the TT-BT algorithm achieves the maximum codebook-based passive and analog beamforming gain. To avoid the limitations imposed the use of the TT property, we design the analog precoder by aligning the analog beamforming to RISs. Moreover, we characterize the rate loss due to codebook-based quantization. Compared with the empirical BT (EBT) algorithm, the proposed TT-BT algorithm achieves better performance with significantly lower training and feedback overheads.

Near-Optimal Designs of Hybrid Precoding and Combining for Massive MIMO Systems From Lattice Decoding

Near-optimal designs consisting of a pair of finite resolution analog precoder and combiner and a baseband encoder for hybrid massive MIMO communication systems are proposed in this paper. Firstly, for any configuration of hybrid MIMO systems, several powerful upper bounds on the maximal achievable rates are presented and are used as guidelines for the proposed designs. Armed with the insights of upper bounds, designing the coefficients for finite resolution analog precoders and combiners is then regarded as a lattice decoding problem, where low-complexity lattice decoders and convex solvers are employed to yield optimal solutions with the best structure for partial connection. Simulation results show that the proposed design achieves within a negligible gap from the rate upper bound using phase shifters with low resolution, while reducing the number of required phase shifters by roughly 20% and significantly outperforming many existing designs at the same time.

Collaborative Management of Resource Allocation and Precoding for Dual-Mode Networks

A sub-6GHz (μWave) and millimeter wave (mmWave) dual-mode network can deliver high data rates while maintaining dependable coverage. This paper proposes a novel resource allocation and precoding algorithm based on collaborative sensing in the dual-mode network. We formulated the collaborative optimization problem of resource allocation and precoding, which maximizes the sum of data rates while meeting minimum acceptable rate requirements and ensuring proportional fairness. Considering the spatial similarity between mmWave channel and μWave channel, the prior information of mmWave channel predicted by μWave band is generated to process sensing weight (i.e., subchannel allocation weight of Markov algorithm), which realizes fast dual-mode network resource allocation. Furthermore, a low complexity two-stage precoding scheme for dual-mode networks is proposed based on μWave spatial information. Low-complexity analog precoding is realized in the first stage based on μWave spatial information. In the second stage, inspired by the correlation between minimum mean square error (MMSE) and mutual information, this paper proposes an alternating optimization algorithm to find a local weighted sum-rate (WSR) optimum with low complexity. Compared with other heuristic algorithms, simulation results demonstrate the apparent advantages of the proposed collaborative sensing optimization algorithm. Most importantly, it can be seen that we can achieve a performance that approaches an upper bound.

Hybrid TH precoding with subarray connection for multi‐user mmWave MIMO systems

AbstractExisting linear algorithms based on subarray connection (SC) structures may cause noise enhancement and performance degradation. To solve this problem, this paper presents a nonlinear hybrid Tomlinson‐Harashima precoding (THP) algorithm based on SC structure, named as THP‐SC algorithm. In the proposed algorithm, the sum‐rate optimization problem can be transformed into two equivalent optimization subproblems including the transmitter optimization subproblem and the receiver optimization subproblem. These two subproblems are easy to solve. Furthermore, the analog precoding and combining matrices of the optimization subproblem are solved by a new improved successive interference cancelation (SIC) algorithm based on the singular value threshold shrinkage (SVS), which can effectively reduce the computational complexity while eliminating the interference. In the end, the digital precoding and combining matrices are obtained by TH method. Through the joint optimization of the transmitter and receiver matrices in the system, the optimal solution is obtained by alternate iteration of the matrices. In this paper, a more realistic line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) channel system is also considered. Simulations verify that the proposed algorithm can improve the spectral efficiency and energy efficiency to some extent with a comparable complexity compared to the previous works.

Low‐complexity unsupervised learning‐based hybrid precoding for massive MIMO systems

AbstractHybrid precoding can significantly reduce the number of required radio frequency (RF) chains in massive MIMO systems. However, due to the large number of antennas and the high‐dimensional channel, the existing hybrid precoding algorithms usually require very high complexity. In this paper, we propose a convolutional neural network (CNN)‐based hybrid precoding scheme by utilizing unsupervised learning to reduce both the computational complexity and space complexity. Specifically, we first design a low‐complexity CNN structure to achieve the analog precoder, where the size of the convolution kernel and the number of channels are reduced by the further improved inception network. Then, we obtain the digital precoder by the classical zero‐forcing (ZF) algorithm. Simulation results show that, the proposed CNN‐based hybrid precoding can approach the sum‐rate performance of the classical two‐stage hybrid precoding but has much lower complexity.

Optimal Analog Precoder Design for Hybrid Beamforming is Possible

Hybrid beamforming is a cost-effective solution for millimeter wave massive multiple-input-multiple-output communications, which highly reduces the number of required radio frequency chains by jointly using a digital precoder and an analog precoder. The digital precoder can be optimized by using the classical least squares method. However, due to the discrete resolution of analog phase shifters, the analog precoder design is a high-dimensional integer programming problem, for which the exsiting methods can only provide suboptimal solutions. In this paper, we first show that optimal analog precoder design is possible in typical antenna settings. Specifically, we show that this high-dimensional problem can be divided into multiple low-dimensional subproblems in parallel and there exists an analytical lower bound for each subproblem that can be efficiently utilized to obtain the optimal solution by using the branch and bound method. Numerical results show that the proposed hybrid precoder with an optimal analog precoder can achieve 98 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> performance of the fully digital solution in a typical antenna setting.

Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO

Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging, severely degrading the performance of conventional spatial division multiple access. To improve the robustness against CSI imperfection, this paper proposes a deep learning (DL)-based rate-splitting multiple access (RSMA) scheme for RIS-aided Tera-Hertz multi-user MIMO systems. Specifically, we first propose a hybrid data-model driven DL-based RSMA precoding scheme, including the passive precoding at the RIS as well as the analog active precoding and the RSMA digital active precoding at the base station (BS). To realize the passive precoding at the RIS, we propose a Transformer-based data-driven RIS reflecting network (RRN). As for the analog active precoding at the BS, we propose a match-filter based analog precoding scheme considering that the BS and RIS adopt the LoS-MIMO antenna array architecture. As for the RSMA digital active precoding at the BS, we propose a low-complexity approximate weighted minimum mean square error (AWMMSE) digital precoding scheme, and further design a model-driven deep unfolding active precoding network (DFAPN) by combining the proposed AWMMSE scheme with DL. Then, to acquire accurate CSI at the BS for the investigated RSMA precoding scheme to achieve higher spectral efficiency, we propose a CSI acquisition network (CAN) with low pilot and feedback signaling overhead. The proposed DL-based RSMA scheme for RIS-aided Tera-Hertz multi-user MIMO systems can exploit the advantages of RSMA and DL to improve the robustness against CSI imperfection, thus achieving higher spectral efficiency with lower signaling overhead.

Design of bitstream-based adaptive-connected array architecture for mmWave Multiuser MIMO systems

We investigate a bitstream-based adaptive-connected massive multiple-input multiple-output (MIMO) architecture that trades off between high-power full-connected and low-performance sub-connected hybrid precoding architectures. The proposed adaptive-connected architecture which enables each data stream to be computed independently and in parallel, consists of fewer phase shifters (PS) and switches than the other adaptive-connected architectures. With smaller array groups, the proposed architecture uses fewer PS and switches, so that its power consumption gradually decreases in millimeter wave (mmWave) Multiuser MIMO (MU-MIMO) system. To fully demonstrate the performance of the proposed architecture in mmWave MU-MIMO system with practical constraints, we combine the connection-state matrix with the hybrid precoders and combiners to maximize energy efficiency (EE) of the system equipped with the proposed architecture. We then propose the hybrid precoding and combining (HPC) scheme suitable for multi-user and multi-data streams which utilizes the SCF algorithm to obtain the constant modulus of the analog precoder at convergence. In the digital precoding and combining stage, the digital precoder and combiner are designed to reduce the amount of computation by utilizing the singular value decomposition (SVD) of corresponding equivalent channel. In the mmWave MU-MIMO-OFDM system equipped with the proposed architecture, with the increase of the total number of data streams, simulation results demonstrate that we can exploit the proposed HPC scheme to achieve better EE than the traditional hybrid full-connected architecture exploiting some existing schemes.

Developing novel channel estimation and hybrid precoding in millimeter-wave communication system using heuristic-based deep learning

The digital or analog precoders are not carried the optimal energy efficiency in the mm-wave massive MIMO and so, every antenna is needed with one radio frequency chain in the system. Based on this view, a cost-efficient technique is developed for hybrid precoding. From this technique, the short dimensional precoding is obtained from the high dimensional beam-formers in the digital domain for steering antenna elements. Therefore, the main intention of this paper is to develop a channel estimation and hybrid pre-coding model for the mm-wave massive MIMO communication system. The channel estimation phase is performed by the Adaptive Deep Convolutional Neural Network (A-Deep CNN), which covers both channel estimation and channel reconstruction. The introduction of a hybrid meta-heuristic algorithm with Forest-Tunicate Swarm Algorithm (F-TSA) is used for enhancing A-Deep CNN that is adaptable for efficient channel estimation. Once the channel estimation is done, the deployment of Optimized Recurrent Neural Networks (O-RNN) is used for hybrid precoding. Simulation results demonstrate that the proposed A-Deep CNN-based channel estimation scheme outperforms the existing schemes in terms of the Normalized Mean-Squared Error (NMSE) and spectral efficiency, while the O-RNN-hybrid precoder design method has better spectral efficiency performance than other methods.

Overhead-Free Blockage Detection and Precoding Through Physics-Based Graph Neural Networks: LIDAR Data Meets Ray Tracing

In this letter, we address blockage detection and precoder design for multiple-input multiple-output (MIMO) links, without communication overhead required. Blockage detection is achieved by classifying light detection and ranging (LIDAR) data through a physics-based graph neural network (GNN). For precoder design, a preliminary channel estimate is obtained by running ray tracing on a 3D surface obtained from LIDAR data. This estimate is successively refined and the precoder is designed accordingly. Numerical simulations show that blockage detection is successful with 95% accuracy. Our digital precoding achieves 90% of the capacity and analog precoding outperforms previous works exploiting LIDAR for precoder design.

Hybrid Precoding Applied to Multi-Beam Transmitting Reconfigurable Intelligent Surfaces (T-RIS)

In this work, we study hybrid precoding techniques applied to multi-user Transmitting Reconfigurable Intelligent Surface (T-RIS) systems. The T-RIS considered here is a large array of electronically reconfigurable antenna elements illuminated by a small set of active sources. When it comes to digital signal-processing techniques applied to T-RIS systems, it is necessary to consider realistic models to bridge the gap with theoretical results. For this reason, we propose a multi-beam T-RIS propagation model with strong phase quantization constraints and limited beam codebooks. First, the proposed model is validated by characterizing a Ka-band T-RIS. Then, we optimize the quad-beam T-RIS structure by tuning the focal distance between the lens and the focal sources according to two metrics: (i) the per-user antenna gain (analog-only approach), and (ii) the per-user average rate (hybrid digital/analog approach). For both indicators, the system performance is evaluated in a multi-user scenario by assuming imperfect channel state information. We show that considering only the analog precoder is sufficient to optimize the T-RIS. However, the fully hybrid precoding scheme is required to deal with inter-user interference. We propose a codebook-aware optimization that improves the aperture efficiency of the T-RIS system.

Efficient Hybrid A/D Beamforming for Millimeter-Wave Systems Using Butler Matrices

Hybrid analog/digital (A/D) beamforming architectures are low complexity alternatives to fully digital designs in large antenna setups. Recent research efforts have been set out to find low-complexity algorithms, which have low implementation complexity in the analog domain. In this paper, we propose a two-stage hybrid precoding design assuming hardware constraints that avoid the use of adaptive phase-shifters (PSs) by introducing a novel combination of Butler matrices (BMs) to feed a uniform planar array (UPA). With several fixed beams, we propose an algorithm to design the analog precoding matrix in the first stage, while in the second stage, hybrid-beamforming (HBF)-weighted minimum mean square error (WMMSE) is proposed for baseband precoding by taking into account the analog precoding matrix designed in the first stage. The proposed algorithm shows fast convergence, thanks to the analog precoder design, which helps the HBF-WMMSE algorithm to converge within a few iterations. Compared with the classical matched filter (MF) and minimum mean square error (MMSE) solutions, HBF-WMMSE outperforms the latter in terms of sum-rate, especially in the low signal-to-noise-ratio (SNR) regime. Simulation results further show that the partially connected Butler matrices (PCBMS) approach implemented with fixed-PSs exhibit superior energy-efficiency while maintaining higher spectral-efficiency as compared to the partially connected analog phase shifting (PCAPS) network implemented with variable-PSs.