VTC 2025 Spring Conference’s Shorts

Quantum-Annealing-aided Multi-user Detection in Overloaded MIMO-OFDM systems

We present performance evaluation of quantum-annealing-aided multi-user detection (QA-aided MUD) in overloaded and fully loaded multi-input multi-output systems with orthogonal frequency division multiplexing (MIMO-OFDM). QA-aided MUD is a method of iterative multi-user detection (MUD) accelerated by using QA. We previously applied this method to single-input single-output (SISO) single-carrier non-orthogonal multiple access (SC-NOMA) and demonstrated that it achieves error rate and convergence performance comparable to the conventional iterative MUD based on the MAP algorithm. In this study, we have newly developed QA-aided MUD for MIMO-OFDM and conducted performance evaluation under the 3GPP UMa channel model by employing the D-Wave qquantum annealer and other digital annealing techniques. The evaluation results show that the block error rate (BLER) obtained by QA-aided MUD outperforms that of conventional low-complexity MUDs, particularly in overloaded scenarios. It is also observed that the annealing parameters significantly impact BLER in some conditions. Additionally, we present bit error rate (BER) analysis using a few instances, employing both the D-Wave quantum annealer and other digital annealing techniques. The results demonstrate that QA-aided MUD with each annealing technique avoids the error floor and can achieve error-free performance. Finally, we discuss future perspectives by evaluating the computational complexity and computation time of QA-aided MUD.Quantum-Annealing-aided Multi-user Detection in Overloaded MIMO-OFDM systemsKouki Yonaga, National Institute Of Information And Communications Technology; Kenichi Takizawa, nict

Over-the-Air MIMO Autoencoder Based Simultaneous Multiple Access for Error-Tolerant and Hi...

In this paper, we propose an over-the-air MIMO autoencoder-based simultaneous multiple access (OA-MIMO-AE-based SMA) scheme for applications that require high-efficiency transmission but can tolerate certain errors. Unlike existing non-orthogonal multiple access schemes, the proposed SMA scheme conducts simultaneous transmission to exploit the superposition nature of the wireless multiple access channel and transform it into an equivalent wireless linear processing layer (WLPL) of the MIMO autoencoder. The receiver aggregates the signals from different transmitters by computing their weighted sum via OFDM over-the-air computation (AirComp), which can fully exploit the correlations among the transmitted data to significantly reduce the communication cost. An alternating algorithm for OFDM over-the-air computation is proposed for accurate data fusion. Benefiting from the MIMO autoencoder, the receiver then recovers each transmitter’s data from their fusion version in parallel, and the proposed SMA scheme can be optimized using the end-to-end method. Simulation results demonstrate that the proposed OA-MIMO-AE-based SMA scheme achieves accurate transmission with a lower communication cost than existing schemes such as OFDMA and NOMA.Over-the-Air MIMO Autoencoder Based Simultaneous Multiple Access for Error-Tolerant and High-Efficiency CommunicationZheyuan Zhou, Zhejiang University; An Liu, College of ISEE, Zhejiang University

Null Space Projection-Based Hybrid Beamforming for Multi-User Massive MIMO

This study employs an array-of-subarrays (AoSA) hybrid beamforming (HBF) architecture in ultra-massive multiple-input multiple-output (UM-MIMO) systems to enhance the total achievable rate. Our primary objective is to mitigate the strong multi-user interference (MUI) through the design of null-space projection (NSP)-based HBF scheme, which involves two stages: (i) RF beamforming based on introducing beam perturbations to steer beams in the null space of interfering users, and (ii) baseband MU precoding stage based on the instantaneous effective channel to mitigate the residual MUI by a regularized zero-forcing (RZF) technique. To solve this challenging non-convex optimization problem, we propose a swarm intelligence-based sequential optimization solution that finds the optimal beam perturbations while adhering to the directivity degradation constraints for the beams in each user direction. The illustrative results depict the high achievable rate by using the proposed NSP scheme over maximum-directivity beamforming (MBF) irrespective of the users’ angular locations, which can be a promising beamforming solution in future sub-Terahertz (sub-THz) UM-MIMO systems.Null Space Projection-Based Hybrid Beamforming for Multi-User Massive MIMOMobeen Mahmood, Yuanxing Zhang, Tho Le-Ngoc, McGill University

Massive MIMO Symmetric SOR Neural Detector

In this paper, a deep learning (DL)-based detector, namely SSOR-Net, is proposed for massive MIMO systems. SSOR-Net enhances the conventional Symmetric Successive Over-Relaxation (SSOR) method while maintaining low complexity. These vectors are optimized through network training, using DL techniques to maximize the detector’s effectiveness. Extensive simulations and complexity analysis reveal that SSOR-Net achieves performance comparable to the optimal OAMP-Net while offering significantly lower complexity. Furthermore, SSOR-Net outperforms MMSE detection, traditional SSOR methods, and other DL-based schemes, particularly in scenarios where the number of transmit antennas approaches that of the receive antennas.Massive MIMO Symmetric SOR Neural DetectorS. Pourmohammad Azizi, Shyi-Chyi Cheng, Hoang-Yang Lu, National Taiwan Ocean University

Joint Optimization for Power-Constrained MIMO Systems: Is Low-Resolution DAC Still Optimal...

This paper explores the joint optimization of precoding, antenna selection, and transmit power within a fixed power budget at a base station (BS). We aim to maximize the sum spectral efficiency in downlink multi-user multiple-input multiple-output systems. The problem is split into two separate sub-problems: joint optimization of antenna selection and precoding direction, and optimization of the transmit power. We then vectorize the precoding matrix and apply approximation techniques to handle the challenges of the problem. We find a superior Lagrangian stationary point to solve the precoding direction sub-problem, and use gradient descent to solve the transmit power sub-problem. Our simulations confirm the algorithm’s effectiveness and show that medium-resolution digital-to-analog converters (DACs) with 6 ∼10 bits can be more power-efficient than the commonly assumed 3 ∼5 bits when the total power consumption at the BS is considered.Joint Optimization for Power-Constrained MIMO Systems: Is Low-Resolution DAC Still Optimal?Jiwon Sung, Seokjun Park, Jinseok Choi, Korea Advanced Institute of Science and Technology

Joint Hybrid Beamforming Design for IRS-Assisted mmWave Full-Duplex MIMO Systems

This paper proposes a joint hybrid beamforming design for intelligent reflecting surface assisted (IRS-JHBF) millimeter wave (mmWave) full-duplex (FD) multiple-input multiple-output (MIMO) systems. The proposed IRS-JHBF consists of analog beamformers, digital precoder/combiner and IRS. The analog beamformers are designed by angle-of-departure (AoD) and angle-of arrival (AoA) information of mmWave channel. Then, we employ singular value decomposition (SVD) and minimizing the mean square error (MMSE) to complete the digital precoder and combiner. Finally, the IRS phase shifts are optimized via the multivariate particle swarm optimization (MV-PSO) algorithm to maximize the system sum rate. Simulation results demonstrate that the proposed IRS-JHBF system significantly eliminates the self-interference and increases the achievable sum rate.Joint Hybrid Beamforming Design for IRS-Assisted mmWave Full-Duplex MIMO SystemsYunfei Wang, Southeast University; Jianing Zhao, Sourheast University; Pubo Bao, XinMing Zhao, Chen Wang, Southeast University

Joint Detection and Angle Estimation for Multiple Jammers in Beamspace Massive MIMO

In this paper, we study the joint detection and angle estimation problem for beamspace multiple-input multiple-output (MIMO) systems with multiple random jammers. An iterative low-complexity generalized likelihood ratio test (GLRT) is proposed by transforming the composite multiple hypothesis test on the projected vector into a series of binary hypothesis tests based on the spatial covariance matrix. In each iteration, the detector implicitly inhibits the mainlobe effects of the previously detected jammers by utilizing the estimated angles and average jamming-to-signal ratios. This enables the detection of a new potential jammer and the identification of its corresponding spatial covariance. Simulation results demonstrate that the proposed method outperforms existing benchmarks by suppressing sidelobes of the detected jammers and interference from irrelevant angles, especially in medium-to-high jamming-to-noise ratio scenarios.Joint Detection and Angle Estimation for Multiple Jammers in Beamspace Massive MIMOPengguang Du, Cheng Zhang, Southeast University; Changwei Zhang, Zhilei Zhang, Purple Mountain Laboratories; Yongming Huang, Southeast University

Joint Channel Extrapolation and Scattering Environment Sensing for Multi-user TDD Massive ...

In this paper, we consider joint channel extrapolation and scattering environment sensing for multi-user time-division duplexing (TDD) massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. Unlike conventional angle-delay domain channel modeling techniques, we adopt a location domain channel modeling approach to leverage the spatial overlap of scatterers across different users. By exploiting this, we propose a novel two-stage joint multi-user channel extrapolation and scattering environment sensing algorithm. In the coarse estimation stage, a low complexity Spatial and Temporal Multiple Signal Classification (ST-MUSIC) algorithm is utilized to perform independent channel extrapolation and scatterer localization for each user. In the refined estimation stage, an aggregated location grid constructed from the coarse estimation result is used to enable a sparse representation of the location domain channels of all users. And by combining the inverse-free variational Bayesian inference (IF-VBI) and the expectation maximization (EM) algorithm, an EM-IF-VBI algorithm is designed to jointly refine the location domain channel coefficients and aggregated location grid of all users, which can exploit the spatial overlap of scatterers across different users to simultaneously achieve more accurate channel extrapolation and scattering environment sensing. Simulation results show that our proposed method significantly outperforms existing baseline methods.Joint Channel Extrapolation and Scattering Environment Sensing for Multi-user TDD Massive MIMO-OFDM SystemsYufan Zhou, Zhejiang University; An Liu, College of ISEE, Zhejiang University

Federated Learning Based Near-Field Channel Estimation for XL-MIMO Communications

Extremely large-scale massive MIMO (XL-MIMO) is foreseen as a promising technology to achieve ultra-low latency, high data transmission speed, and extremely low error rate in future 6G networks. The increases in antenna apertures and the use of higher frequencies (millimeter-wave and sub-THz) in XL-MIMO significantly extend the Rayleigh distance, thereby enhancing the prevalence of near-field (NF) communications. Unfortunately, existing far-field channel models struggle to accurately capture both line-of-sight (LoS) and non-line-of-sight (NLoS) propagation paths in the presence of NF effects. Moreover, the huge number of antenna elements greatly increases computational demands and complicates channel estimation tasks. In this paper, we propose a federated learning (FL)-based near-field channel estimation (NFCE) framework for mixed LoS/NLoS environments. In this framework, we employ a federated deep residual learning (FDRL)-based convolutional neural network (CNN) architecture, where only a subset of local devices participates in the distributed training process. By utilizing the complex convolution and a few residual blocks, this framework reduces the effect of signal noise while minimizing communication overhead and computational complexity. Simulation results demonstrate that our proposed framework significantly improves NFCE performance in terms of normalized mean square error and bit error rate compared to selected benchmark schemes.Federated Learning Based Near-Field Channel Estimation for XL-MIMO CommunicationsSree Das, Benoit Champagne, McGill University

Efficient Near-field Localization for Hybrid Analog and Digital UM-MIMO Systems

Hybrid analog and digital ultra-massive multiple-input multiple-output (UM-MIMO) has become one of the key enabling technologies for the upcoming 6G. As the number of antennas of UM-MIMO systems increases and the array size grows, the near-field assumption should be considered instead of the far-field assumption. Therefore, more complex algorithms are required to estimate the DoA and distance that describe the characteristics of the source. In this paper, we propose an efficient near-field localization algorithm for hybrid analog and digital UM-MIMO systems, which reduces the high computational complexity of existing algorithms in the near field by decoupling directions of arrival (DoA) and distance estimation. Firstly, by designing the digital combiner, we estimate the DoA using the central subarray received signal. Next, we design a set of analog combiners that depend solely on the distance, and apply signal-to-noise ratio (SNR) determination to narrow down the range for distance estimation. Finally, digital combiners are applied to perform an exhaustive search within the narrowed distance range, enabling efficient localization. Simulation results show that the localization performance of our proposed algorithm is superior to the existing algorithms, and its computational complexity is significantly reduced.Efficient Near-field Localization for Hybrid Analog and Digital UM-MIMO SystemsYanran Sun, Chuang Yang, Beijing University of Posts and Telecommunications; Mugen Peng, Beijing University of Posts & Telecommunications