MIMO-OFDM Systems Research Articles

An efficient infinity norm minimization algorithm for under-determined inverse problems

The problem of solving under-determined systems of linear equations with minimum peak magnitude (ℓ∞ norm) has numerous applications in signal processing. These include Peak-to-Average Power Ratio (PAPR) reduction in MIMO-OFDM systems, vector quantization, approximate nearest neighbor search, optimal control in robotics, and power grid optimization. Several methods have been proposed to address this problem, but they often face limitations in computational speed or representation accuracy. Some methods also impose constraints on the frame matrix, such as restrictions on the type of its entries or its aspect ratio. In this paper, we present the Fast Iterative Peak Shrinkage Algorithm (FIPSA), which iterates over feasible solutions to consistently reduce peak magnitude and provably converge to near-optimal solutions. Our experimental results, conducted across various frame matrix types and aspect ratios, demonstrate that FIPSA consistently achieves near-minimal ℓ∞ norm values. In addition, it operates 1.3 to 7.3 times faster than previous methods, while maintaining an average representation error of 10−15. Notably, these advancements are achieved without imposing any constraints on the frame matrix.

An Adaptive CSI Feedback Model Based on BiLSTM for Massive MIMO-OFDM Systems

An Adaptive CSI Feedback Model Based on BiLSTM for Massive MIMO-OFDM Systems

Transformer-empowered receiver design of OFDM communication systems

With deep learning, we perform channel estimation and signal detection in massive Multiple Input Multiple Output (MIMO)-Orthogonal Frequency Division Multiplexing (OFDM) systems in this paper. Specifically, we design and extend the basic framework of receivers for MIMO-OFDM systems in an end-to-end approach. A Transformer-based MIMO-OFDM receiver called TCD-Receiver is proposed, which introduces a multi-attention mechanism to learn the channel characteristics by introducing a generic and flexible Transformer network structure. The network parameters are updated based on the relationship between the received signal and the original signal, where the final signal information is obtained without explicit channel estimation and the predicted transmit bits are directly output. The experimental results show that the TCD-Receiver proposed can effectively solve the channel distortion and detect the transmitted signals compared with the traditional communication receivers, and its performance can be comparable to that of the traditional OFDM receivers, and it also has obvious advantages in combating the complex and difficult-to-model channel environment as well as the nonlinear interference factors.

A Tensor-Based Signal Processing for ISAC Using C-DRCNN in RIS-Assisted mmWave MIMO-OFDM Systems

A Tensor-Based Signal Processing for ISAC Using C-DRCNN in RIS-Assisted mmWave MIMO-OFDM Systems

Automatic Identification of Space-Time Block Coding for MIMO-OFDM Systems in the Presence of Impulsive Interference

Automatic Identification of Space-Time Block Coding for MIMO-OFDM Systems in the Presence of Impulsive Interference

An Improved Pelican Optimization Algorithm Based Residual Recurrent Neural Network for Channel Estimation with Hybrid Precoder in MIMO-OFDM System

An Improved Pelican Optimization Algorithm Based Residual Recurrent Neural Network for Channel Estimation with Hybrid Precoder in MIMO-OFDM System

Manifold Optimization-Based Data Detection Algorithm for Multiple-Input–Multiple-Output Orthogonal Frequency-Division Multiplexing Systems under Time-Varying Channels

Recently, multiple-input–multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems have gained significant attention in the field of wireless communications. The utilization of the Riemannian manifold has become prevalent in MIMO-OFDM systems. However, the existing data detection algorithms for MIMO-OFDM systems are mostly designed for block fading channels. Additionally, these algorithms often suffer from high computational complexity. In this paper, we propose a data detection algorithm on the basis of Riemannian manifold optimization for MIMO-OFDM systems under time-varying channels. The core concept of this algorithm is to optimize the transmitted signals by solving the manifold optimization problem in the case of time-varying channels. In order to reduce the computational complexity of the algorithm, we improve the proposed algorithm by dividing the transmitted signals into multiple subframes for solving the optimization problem separately and using the pilots to maintain the performance of the algorithm. In the simulation, the performance of multiple proposed algorithms and the forced-zero detection algorithm under different parameter settings are compared. The simulation results show that the proposed algorithm demonstrates good bit error rate and computational complexity performances.

Direct Localization and Synchronization for High-Mobility Agents With Frequency Shifts in MIMO-OFDM Systems

Direct Localization and Synchronization for High-Mobility Agents With Frequency Shifts in MIMO-OFDM Systems

Enhancing PAPR reduction efficiency in MIMO-OFDM systems via selective mapping and metaheuristic algorithms

The relentless evolution of communication systems, driven by the demands of 5G and the impending 6G networks, necessitates heightened data rates and spectral efficiency. orthogonal frequency division multiplexing (OFDM), a form of multicarrier modulation employed in multi-input multi-output (MIMO) systems, stands as a pivotal technology. Yet, OFDM grapples with challenges, notably the peak-to-average power ratio (PAPR) issue. Selective mapping (SLM) has been a favored technique for mitigating PAPR in OFDM, albeit challenged by computational complexities in its pursuit of discovering optimal phase factors. This paper pioneers a transformative approach by integrating metaheuristic algorithms genetic algorithm (GA), particle swarm optimization (PSO), and the innovative fireworks algorithm (FWA) into SLM for PAPR reduction while minimizing computational complexity. Simulation results not only affirm the efficacy of SLM-based techniques but also spotlight the potential of metaheuristic algorithms in addressing PAPR challenges in modern communication systems. The study transcends single-antenna systems, extending to MIMO-OFDM systems based on WiMAX standards, validating the efficacy of these techniques in multi-antenna configurations. Crucially, the FWA, proposed for the first time in this paper, emerges as a robust candidate, striking an enviable balance between computational efficiency and performance, achieving a notable PAPR reduction with a favorable search number.

Spatial Structure Matching-Based DoA Estimation and Tracking for Integrated Sensing and Communication Massive MIMO OFDM System

Spatial Structure Matching-Based DoA Estimation and Tracking for Integrated Sensing and Communication Massive MIMO OFDM System

Quasi-Newton FDE in One-bit Pseudo-Randomly Quantized Massive MIMO-OFDM Systems

Quasi-Newton FDE in One-bit Pseudo-Randomly Quantized Massive MIMO-OFDM Systems

Blind Multi-Level MAP Detection With Phase Noise Compensation in MIMO-OFDM Systems

Blind Multi-Level MAP Detection With Phase Noise Compensation in MIMO-OFDM Systems

Channel Feature Projection Clustering Based Joint Channel and DoA Estimation for ISAC Massive MIMO OFDM System

Channel Feature Projection Clustering Based Joint Channel and DoA Estimation for ISAC Massive MIMO OFDM System

Deep Learning Based Fingerprint Positioning for Multi-Cell Massive MIMO-OFDM Systems

Deep Learning Based Fingerprint Positioning for Multi-Cell Massive MIMO-OFDM Systems

Leveraging neuro-inspired AI accelerator for high-speed computing in 6G networks.

The field of wireless communication is currently being pushed to new boundaries with the emergence of 6G technology. This advanced technology requires substantially increased data rates and processing speeds while simultaneously requiring energy-efficient solutions for real-world practicality. In this work, we apply a neuroscience-inspired machine learning model called echo state network (ESN) to the critical task of symbol detection in massive MIMO-OFDM systems, a key technology for 6G networks. Our work encompasses the design of a hardware-accelerated reservoir neuron architecture to speed up the ESN-based symbol detector. The design is then validated through a proof of concept on the Xilinx Virtex-7 FPGA board in real-world scenarios. The experiment results show the great performance and scalability of our symbol detector design across a range of MIMO configurations, compared with traditional MIMO symbol detection methods like linear minimum mean square error. Our findings also confirm the performance and feasibility of our entire system, reflected in low bit error rates, low resource utilization, and high throughput.

A novel iterative detection method based on a lattice reduction-aided algorithm for MIMO OFDM systems

The lattice reduction-aided algorithm has received broad attention from researchers since it operates as a maximum likelihood receiver with better system performance for multiple-input multiple-output orthogonal frequency division multiplexing systems and contains a full diversity. A novel iterative detection algorithm canceling parallel iterations that employ the lattice reduction-aided approach is proposed. Soft information is exchanged through the detector itself. Its iteration occurs inside the detector, which reduces much of the exchange cost between the multiple-input multiple-output orthogonal frequency division multiplexing detector and the turbo decoder. Since the parallel interference cancellation algorithm is constrained by the accuracy of the initial value of the detection, it is easy to form error propagation after several iterations. Due to the lattice reduction-aided algorithm, its performance is approximated with the maximum likelihood algorithm. Therefore, the lattice reduction-aided algorithm is introduced into the parallel interference cancellation algorithm to make its detection algorithm more accurate and overcome the effect of error propagation in the manuscript. Simulation results indicate that the proposed algorithm leads to an improvement of 0.8–2 dB when the bit error rate is set to 10–4 when compared to other algorithms.

Turbo message passing based burst interference cancellation for data detection in massive MIMO-OFDM systems

Turbo message passing based burst interference cancellation for data detection in massive MIMO-OFDM systems

Spectral-Efficient and Power-Efficient MIMOOFDM System with Time Diversity for Flat Fading Channel with Arbitrary Doppler Frequency Shift

Spectral-Efficient and Power-Efficient MIMOOFDM System with Time Diversity for Flat Fading Channel with Arbitrary Doppler Frequency Shift

Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems

Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems

Low-complexity Precoding-aided CFO Estimation for ICA-based MIMO OFDM Systems in URLLC

Low-complexity Precoding-aided CFO Estimation for ICA-based MIMO OFDM Systems in URLLC