Massive Multiple-input Multiple-output Orthogonal Frequency-division Multiplexing Research Articles

A rapid fingerprint positioning method based on deep convolutional neural network for MIMO-OFDM systems

The combination of fingerprint positioning and 5G (the 5th Generation Mobile Communication Technology) offers broader application prospects for indoor positioning technology, but also brings challenges in real-time performance. In this paper, we propose a fingerprint positioning method based on a deep convolutional neural network (DCNN) using a classification approach in a single-base station scenario for massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation. The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences. First, the DCNN model is used to determine the sub-area to which the mobile terminal belongs, and then the weighted K-nearest neighbor (WKNN) matching algorithm is used to estimate the position within the sub-area. The positioning performance is simulated in a DeepMIMO indoor environment, showing that the classification DCNN method reduces the positioning time by 77.05% compared to the non-classification method, with only a 1.08% increase in average positioning error.

Sum rate optimization in STAR-RIS assisted multiuser massive MIMO-OFDM VLC systems

Visible Light Communication (VLC) offers a promising solution for future networks, leveraging existing lighting infrastructure in indoor environments. However, VLC requires a direct line of sight to function which can be limiting. Reconfigurable Intelligent Surface (RIS) is a new technology that can bend light and radio waves, addressing this limitation in VLC. RIS come in three types: passive which reflects signals, active that boosts and reflects signals, and Simultaneous Transmission and Reflection (STAR)-RIS which can both reflect and transmit signals simultaneously. STAR-RIS offers the most control over the signal. In this paper, we propose a new multi-user VLC system with a massive Multiple-Input, Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) architecture, leveraging STAR-RIS to optimize data rates and improve coverage, particularly in non-line-of-sight scenarios. We formulate a system model and solve a convex optimization problem to determine the optimal transmission and reflection coefficients for STAR-RIS elements, aiming to maximize the total sum rate for all users. By employing maximum ratio transmission precoding, we minimize interference among users and demonstrate significant performance gains. Simulation results show that our proposed energy splitting-based STAR-RIS configuration outperforms traditional mode selection and time switching approaches with fixed or random coefficients, yielding substantial improvements in data rates and user experience. This study offers the first detailed exploration of STAR-RIS in VLC systems, highlighting its potential for future high-speed, multi-user communication networks. Our findings set the stage for further research into optimizing VLC systems using STAR-RIS, particularly in complex environments with non-line-of-sight users and massive MIMO-OFDM configurations.

Low complexity hybrid beamforming for downlink multi-user MIMO OFDM systems

Hybrid beamforming is critical for massive multiple input multiple output (MIMO) to achieve high spectral efficiency at reduced hardware cost. Existing hybrid beamforming schemes either design the analog beamforming and digital beamforming separately, or jointly optimize them through alternating optimization. Such strategies incur non-negligible performance degradation or huge computational complexity in optimization, which is especially evident with the partially connected architecture. In this work, we consider a general downlink multi-user MIMO orthogonal frequency division multiplexing (OFDM) system with the hybrid beamforming architecture, where finite-resolution phase shifters are adopted. We propose an efficient beamforming strategy on the sum rate maximization criterion. Rather than eliminating the interference entirely by digital beamforming, we optimize the analog beamforming by considering the interference at the same time. We consider a fixed digital beamforming structure to reduce the overall complexity. Given the fact of finite-resolution phase shifters, the analog beamforming is optimized over discrete phase values. Compared to existing hybrid beamforming designs, the proposed algorithm achieves much improved performance. By applying the average channel covariance to the analog beamforming design, the computational complexity of the proposed algorithm is further reduced. Meanwhile, the proposed algorithm is applicable to both fully and partially connected architectures.

Novel Hybrid SOR- and AOR-Based Multi-User Detection for Uplink M-MIMO B5G Systems

The Internet of Things (IoT) is one of the most important wireless sensor network (WSN) applications in 5G systems and requires a large amount of wireless data transmission. Therefore, massive multiple-input multiple-output (M-MIMO) has become a crucial type of technology and trend in the future of beyond fifth-generation (B5G) wireless network communication systems. However, as the number of antennas increases, this also causes a significant increase in complexity at the receiving end. This is a challenge that must be overcome. To reduce the BER, confine the computational complexity, and produce a form of detection suitable for 4G and B5G environments simultaneously, we propose a novel multi-user detection (MUD) scheme for the uplink of M-MIMO orthogonal frequency division multiplexing (OFDM) and universal filtered multi-carrier (UFMC) systems that combines the merits of successive over-relaxation (SOR) and accelerated over-relaxation (AOR) named mixed over-relaxation (MOR). Herein, we divide MOR into the initial and collaboration stages. The former will produce the appropriate initial parameters to improve feasibility and divergence risk. Then, the latter achieves rapid convergence and refinement performance through alternating iterations. The conducted simulations show that our proposed form of detection, compared with the BER performance of traditional SOR and AOR, can achieve 99.999% and 99.998% improvement, respectively, and keep the complexity at O(N2). It balances BER performance and complexity with fewer iterations.

One-Bit Downlink Precoding for Massive MIMO OFDM System

Massive multiple-input multiple-output (MIMO) is a key technology in next generation wireless communication. However, the increasing number of radio frequency (RF) chains results in higher cost and power consumption. Given that hundreds or even thousands of transmit antennas are equipped at the base station (BS), low resolution digital-to-analog converters (DACs) are preferred to reduce the power consumption on both DACs and power amplifiers (PAs). Currently, there have been some studies about the application of low-resolution DACs for single-carrier systems. For multi-carrier systems, this problem hasn’t been fully investigated. This paper aims to design a 1-bit downlink precoding algorithm for massive multi-user MIMO orthogonal frequency division multiplexing (OFDM) systems. A nonlinear precoding algorithm is proposed, which can address the non-convex optimization problem with discrete output constraint and guarantee convergence. Meanwhile, the proposed algorithm factors in the different path-losses experienced by different users. Furthermore, some approximation schemes can be applied to bring down the computational complexity of the proposed algorithm further. Simulation results illustrate that our algorithm performs the best among other nonlinear precoding methods in OFDM systems.

Hybrid Message Passing Channel Estimation Algorithm for Massive MIMO-OFDM Systems

In this letter, we propose the Bernoulli two-state Gaussian mixture (B-TSGM) probability model to characterize the angle-delay domain (ADD) channel of the massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system. Based on the hybrid message passing (HMP) rule, we design the HMP-B-TSGM channel estimation algorithm under the structured turbo-compressed sensing (STCS) framework. Simulation results show that the considered model better captures the channel characteristics, and the proposed algorithm outperforms the state-of-the-art methods under a wide range of simulation settings while having the same complexity.

Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems

In massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, a challenging problem is how to predict channel state information (CSI) (i.e., channel prediction) accurately in mobility scenarios. However, a practical obstacle is caused by CSI non-stationary and nonlinear dynamics in temporal domain. In this paper, we propose a spatio-temporal neural network (STNN) to achieve better performance by carefully taking into account the spatio-temporal characteristics of CSI. Specifically, STNN uses its encoder and decoder modules to capture the spatial correlation and temporal dependence of CSI. Further, the differencing-attention module is designed to deal with the non-stationary and nonlinear temporal dynamics and realize adaptive feature refinement for more accurate multi-step prediction. Additionally, an advanced training scheme is adopted to reduce the discrepancy between STNN training and testing. Evaluated on a realistic channel model with enhanced mobility and spherical waves, experimental results show that STNN can effectively improve the accuracy of prediction and perform well with respect to different signal to noise ratios (SNRs). Visualization and testing for unit root illustrate STNN is able to learn CSI time-varying patterns by alleviating series non-stationarity.

Implementation of Omar Pigeon Space-Time (OPST) Algorithm to Mitigate the Interference and Peak-to-Average Power Ratio (PAPR) Using RPR Mobile and HST-HM in the 5G

Nowadays, the 5G parameters play an eminent role in the massive Multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system for enriching high signal to noise ratio (SNR). 5G application has emerged in the role of artificial intelligence for involving the reduction of Peak to Average Power Ratio (PAPR) and Bit Error Rate (BER). In MIMO – OFDM system, the high PAPR is a tremendous drawback during the transmission of bit symbol with the number of sub-carriers in the signal. To avoid Intercarrier Interference (ICI) during transmission of the number of sub-carriers, the Omar Pigeon Space-Time (OPST) algorithm is implemented. Then, to overcome high PAPR in the uplink, the Hybrid Space-Time - Hadamard matrix (HST-HM) techniques are proposed and the Bit Error Rate (BER) is decreased abruptly. 5G parameters and specifications are incorporated in this OPST algorithm for avoiding interference during the data bit transmission in the MIMO – OFDM system. Realtors Property Resource (RPR) mobile app is developed for an experimental display of the information that occurs in the real-time uplink MIMO – OFDM system. Thus, the descriptive analysis and simulated results of PAPR, SNR, and BER are executed using the proposed system of HST with HM in the 5G Communication. The RPR mobile executes the outcomes through the OPST algorithm with a better system performance of the MIMO-OFDM system based on the 5G.

Proactive Eavesdropping in Massive MIMO-OFDM Systems via Deep Reinforcement Learning

It is challenging to monitor suspicious links in massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems with directional beamforming. When the monitor and suspicious user are not in the coverage of the same beam, to implement legitimate monitoring, we propose the proactive eavesdropping scheme in which the monitor acts as a spoofing relay to realize beam misleading and data eavesdropping. In the phase of beam sweep, the beam misleading algorithm induces the transmitter to choose the beam which is beneficial to the monitor by optimizing the relay precoding matrices. In the phase of data transmission, the data eavesdropping algorithm elevates the monitoring rate by optimizing the relay power split factors and power gain factors of all subcarriers. Due to the unknown channel state information between suspicious pairs, we propose to find the optimal precoders and power split factors by the multi-agent deep deterministic policy gradient (MADDPG) algorithm. The simulation results verify the effectiveness and superiority of the proposed eavesdropping scheme.

Impact of Subcarrier Allocation and User Mobility on the Uplink Performance of Multiuser Massive MIMO-OFDM Systems

This paper considers the uplink performance of a multi-user massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system with mobile users. Mobility brings two major problems to a MIMO-OFDM system: inter carrier interference (ICI) and channel aging. In practice, it is common to allot multiple contiguous subcarriers to a user as well as schedule multiple users on each subcarrier. Motivated by this, we consider a general subcarrier allocation scheme and derive expressions for the ICI power, uplink signal to interference plus noise ratio and the achievable uplink sum-rate, taking into account the ICI and the multi-user interference due to channel aging. We show that the system incurs a near-constant ICI power that depends linearly on the ratio of the number of users per subcarrier to the number of subcarriers per user, nearly independently of how the UEs distribute their power across the subcarriers. Further, we exploit the coherence bandwidth of the channel to reduce the length of the pilot sequences required for uplink channel estimation. We consider both zero-forcing and maximal-ratio combining at the receiver and compare the respective sum-rate performances. In either case, the proposed subcarrier allocation scheme leads to significantly higher sum-rates compared to previous work, owing to the near-constant ICI property as well as the reduced pilot overhead.

Wideband hybrid precoder for mmWave multiuser MIMO OFDM communications

Using millimeter wave (mmWave) transmission for massive multiple input multiple output (MIMO) system can improve system performance and effectively reduce the size of the massive antennas array. However, A wideband beamformer design is needed to take advantage of this wideband channel. In this paper, a downlink multi-user massive MIMO orthogonal frequency division multiplexing (MIMO OFDM) system for mmWave communications is proposed. Each subcarrier channel can be approximated as a narrowband clustered channel, so a narrowband precoder can be applied for each subchannel. The hybrid precoder is implemented in a manner so the digital precoder is obtained for each subcarrier, whilst the analog precoder is common for all subcarriers. A modified “joint spatial division/multiplexing” (JSDM) scheme is used to design the precoder, where each user equipped with more than one antenna. The design of the analog precoder is based on the second order channel statistics to reduce the overhead information need to process and fed back and the subcarrier baseband precoder based on the instantaneous channel state information (CSI). Following the approach of Kronecker channel model, the iteration between the analog beamformers design at both end of link can be avoided. Finally testing the system using various numbers of antennas at base station.

A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO

Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-wave (mm-Wave) massive MIMO (multiple-input multiple-output) systems. However, lack of fully digital beamforming in hybrid architectures and short coherence times at mm-Wave impose additional constraints on the channel estimation. Prior works on addressing these challenges have focused largely on narrowband channels wherein optimization-based or greedy algorithms were employed to derive hybrid beamformers. In this paper, we introduce a deep learning (DL) approach for channel estimation and hybrid beamforming for frequency-selective, wideband mm-Wave systems. In particular, we consider a massive MIMO Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system and propose three different DL frameworks comprising convolutional neural networks (CNNs), which accept the raw data of received signal as input and yield channel estimates and the hybrid beamformers at the output. We also introduce both offline and online prediction schemes. Numerical experiments demonstrate that, compared to the current state-of-the-art optimization and DL methods, our approach provides higher spectral efficiency, lesser computational cost and fewer number of pilot signals, and higher tolerance against the deviations in the received pilot data, corrupted channel matrix, and propagation environment.

Comparison on BER Performance Using Linear Filtering Least Square Method and Least Square Method for MIMO-OFDM Based Communication Systems

In Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, the Bit Error Rate is poor by using Linear Filtering Least Square method (LFLS) and Least Square (LS) method for massive MIMO-OFDM systems to extend the strength of the signal and to scale back the noise. Therefore the objective of the project is to maintain better Bit Error Rate performance. Materials and methods: Using Linear Filtering Least Square method, different values are used to enhance the Bit Error Rate (BER). The proposed method is compared with the least square method, the results are obtained by using Matlab simulation software. Results: The performance of Bit Error Rate is calculated by using the Linear Filtering Least Square method and enhanced Least Square method parameters. At 20 dB SNR, the linear filtering least-square provides the BER value is 10-3, whereas the Novel enhanced least-square scheme provides the BER value around 10-5. Conclusion: Based on experimental results and statistical analysis using the independent sample T-test, the Least Square method significantly performs better than the Linear Filtering Least Square method.

BER Analysis and CS-Based Channel Estimation and HPA Nonlinearities Compensation Technique for Massive MIMO System

Massive Multiple Input Multiple Output (MIMO) system has gathered lately a huge interest from researchers and it has been known as a backbone technology for the 5th generation because it can greatly improve the capacity of wireless communication and it can potentially provide energy efficiency and security. Before ensuring the full advantages, massive MIMO system has to overcome many challenges. One of them is to accurate an exact estimation of the Channel Impulse response (CIR) between each transmit-receive antenna pair. The other problem arises when implementing such a system in presence of High Power Amplifiers (HPA) which imposes a nonlinear distortion on the transmitted signals. This paper proposes contributions in three key areas. Firstly, we investigate the nonlinear effects of memoryless HPA, which is modeled using Saleh model, by deriving its Bit Error Rate expression (BER) in massive MIMO Orthogonal Frequency Division Multiplexing (OFDM) system. Secondly, we highlight the necessity of providing the accurate channel characteristics by estimating it using Compressive Sensing (CS) techniques. Moreover, we propose also a CS compensation algorithm based on Orthogonal Matching Pursuit (OMP) to mitigate the nonlinear distortion in the receiver. The proposed CS-based compensation technique has been compared to the Neural Network (NN) pre and post compensation technique and it has shown a good performance not only in terms of BER but also in terms of complexity.

Sparse Channel Estimation via Hierarchical Hybrid Message Passing for Massive MIMO-OFDM Systems

In this paper, we investigate a sparse channel estimation problem for broadband massive multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We propose a hidden Markov model to capture the structured sparsity and temporal dependency characteristic of massive MIMO-OFDM channels in the angle-delay domain, and this probability model exhibits extensive adaptability to different realistic propagation scenarios. Then we solve the channel estimation problem based on a novel optimization framework named constrained Bethe free energy (BFE) minimization, which is valid for a generic statistical model. Under this systematic theoretical framework, a hierarchical hybrid message passing (HHMP) algorithm is proposed to track dynamic channel parameters recursively. The proposed method can adaptively learn the sparse structure and temporal correlation of multiuser channels without requiring the knowledge of hidden Markov channel parameters. Numerical simulations demonstrate that the proposed HHMP algorithm can accurately estimate angle-delay domain channels with reduced iteration times and pilot overhead.

Optimized Precoders for Massive MIMO OFDM Dual Radar-Communication Systems

This paper considers the optimization of a dual-functional radar and communication (RadCom) system with the objective is to maximize its sum-rate (SR) and energy-efficiency (EE) while satisfying certain radar target detection and data rate per user requirements. To this end, novel RadCom precoder schemes that can exploit downlink radar interference are devised for massive multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. First, the communication capacity and radar detection performance metrics of these schemes are analytically evaluated. Then, using the derived results, optimum beam power allocation schemes are deduced to maximize SR and EE with modest computational complexity. The validity of the analytical results is confirmed via matching computer simulations. It is also shown that, compared to benchmark techniques, the devised precoders can achieve substantial improvements in terms of both SR and EE.

Quantized Polar Transmitters for Power Efficient Massive MIMO Systems

This letter investigates the feasibility of the quantized digital polar transmitter architecture in a downlink multi-user massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) system, where a low amplitude resolution and a moderate phase resolution are utilized. We derive a lower bound on the average sum-rate achievable with Gaussian signaling inputs and linear precoding by a simple diagonal approximation for the quantization distortion. The analysis and simulations demonstrate that the quantized polar transmitter can enable excellent performance in terms of average sum-rate throughput, symbol error rate (SER), and out-of-band (OOB) emission level, thus providing an attractive option for the traditional Cartesian architecture.

Algorithm and VLSI Design for 1-Bit Data Detection in Massive MIMO-OFDM

The use of low-resolution data converters in the radio-frequency (RF) chains of all-digital massive multiple-input multiple-output (MIMO) basestations promises significant reductions in power consumption, hardware costs, and interconnect bandwidth. We propose a quantization-aware data-detection algorithm which mitigates the performance loss of 1-bit quantized massive MIMO orthogonal frequency-division multiplexing (OFDM) systems. Since the system performance heavily depends on the quality of channel estimates, we also develop a nonlinear 1-bit channel estimation algorithm that builds upon the proposed data detection algorithm. We show that the proposed algorithms significantly outperform linear data detectors and channel estimators in terms of bit error rate. For the proposed nonlinear data detection algorithm, we develop a very large scale integration (VLSI) architecture and present implementation results on a Xilinx Virtex-7 field programmable gate array (FPGA). Our implementation results are, to the best of our knowledge, the first for 1-bit massive MU-MIMO-OFDM systems and demonstrate comparable hardware efficiency with respect to state-of-the-art linear data detectors designed for systems with high-resolution data converters, while achieving lower bit error rate.

Compressed channel estimation for massive MIMO-OFDM systems over doubly selective channels

Doubly selective (DS) channel estimation for the downlink massive multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is a challenging problem, due to the requirements on high pilots overhead and prohibitive complexity. In this paper, by exploiting the highly correlated spatial structure of the obtained array response vectors and sparsity of the multipath signal components of the massive MIMO-OFDM channels, a modified spatial basis expansion model (modified-SBEM) is introduced. Thus, using complex exponential (CE-) modified-SBEM (i.e., modified CE-SBEM) can improve the resolution of the angles of departures (AoDs) information to represent the downlink with far fewer parameter dimensions, since the AoDs are much slower than path gains. Subsequently, we jointly design the effective pilot power and pilot placement for sparse channel estimation by means of an extended model. Our design is based on the block-coherence and sub-coherence simultaneous minimization of the measurement matrix associated with the massive MIMO-OFDM system pilot subcarriers. Furthermore, we leverage the sparse nature of the massive MIMO-OFDM system to formulate the quantized AoDs estimation into a block-sparse signal recovery problem, where the measurement matrix is designed based on the estimated virtual AoD. Thus, a new algorithm namely, generalized quasi-block simultaneous orthogonal matching pursuit (gQBSO), is introduced to solve the problem by providing sparse signal reconstruction solution. Simulation results demonstrate that the proposed scheme can effectively estimate the DS channel for massive MIMO-OFDM systems compared with other existing algorithms. For example, at SNR=20 dB for K=4 users, Doppler shift=0.093 with NT=32 antenna size, the adaptive-QBSO algorithm with G-SBEM and the proposed gQBSO with modified-SBEM can realize approximately 75.44%and 85.14% of the NMSE achieved by the oracle estimator with modified-SBEM.

Pilot Contamination in Massive MIMO Induced by Timing and Frequency Errors

Pilot contamination in the uplink (UL) puts asymptotic limits on the downlink (DL) spectral efficiency in time-division duplex (TDD) massive multiple-input multiple-output (MIMO) systems relying on TDD channel reciprocity. Pilot contamination can be induced by the UL pilot reuse across different neighboring cells. In this paper, we show that receiver front-end impairments also contribute to pilot contamination. In particular, we consider a TDD multi-user (MU) massive MIMO orthogonal frequency-division multiplexing (OFDM) system, where either time-division multiplexed (TDM) pilots or frequency-division multiplexed (FDM) pilots are utilized for UL channel sounding. We endeavor to characterize the impacts of the residual timing offsets (TOs) and the carrier frequency offsets (CFOs) on the DL performance of the massive MIMO-OFDM system. Closed-form expressions of the asymptotic DL MU spectral efficiencies are derived in the presence of both TOs and CFOs under different scenarios. Our analytical results reveal how the residual TOs and CFOs destroy the orthogonality among the UL training sequences from different users and give rise to pilot contamination. Specifically, we show that the DL spectral efficiencies become bounded even when the number of antennas goes toward infinity. Furthermore, to alleviate the impacts of TOs and CFOs, we propose different pilot decontamination methods based on our analyses for both the TDM pilots and the FDM pilots. Numerical simulation results corroborate our analyses and designs.