VTC 2025 Spring Conference’s Shorts

Deep Unfolding Based Simplified Information Geometry Approach for Massive MIMO-OFDM Channe...

In this paper, we investigate the channel estimation in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. The recently proposed simplified information geometry (SIG) algorithm offers a promising solution for channel estimation with relatively low computational complexity. However, the damping factors used in the SIG algorithm are based on a heuristic strategy, which results in inconsistent performance. To address this issue, we propose a deep unfolding based SIG (DU-SIG) approach in this paper. Specifically, each iteration of the SIG algorithm is unfolded into a layer-wise structure resembling a neural network and the damping factors are optimized based on deep unfolding technique. Notably, the optimized damping factors can be directly integrated into the original SIG algorithm to improve the performance of channel estimation without increasing the computational complexity. Simulation results validate the effectiveness and superiority of our proposed algorithm.Deep Unfolding Based Simplified Information Geometry Approach for Massive MIMO-OFDM Channel EstimationChun Cai, Fuqian Yang, Purple Mountain Laboratories; Jiyuan Yang, Southeast University; Hebing Wu, Jinlin Zhang, Purple Mountain Laboratories; Xiqi Gao, Southeast University

CRB Oriented Transmit Waveform Optimization for One-Bit MIMO Radar

We consider the transmit waveform design for a collocated multiple-input multiple-output (MIMO) radar system, where one-bit analog-to-digital converters (ADCs) are deployed to enable a low-lost and power-efficient hardware implementation. Focusing on improving the target parameter estimation performance, we first derive a novel one-bit Cramér-Rao bound (CRB) metric by exploiting the Bussgang-based linear signal model and the worst-case Gaussian assumption. Then, based on the maximum likelihood principle, we develop a practical one-bit parameter estimation method to approach the derived CRB performance. Next, by minimizing the above one-bit CRB objective subject to a total power constraint, we formulate a transmit waveform optimization problem, which is highly nonconvex due to the one-bit quantization. To solve this problem, a majorization-minimization framework integrated with a projected gradient descent (MMPGD) algorithm is carefully designed whose convergence and complexity analysis are also provided. Finally, numerical results substantiate the tightness of the proposed one-bit CRB and the effectiveness of the MMPGD algorithm, where clear performance improvements can be achieved compared to the existing benchmark schemes.CRB Oriented Transmit Waveform Optimization for One-Bit MIMO RadarQi Lin, Hong Shen, Wei Xu, Southeast University; Chunming Zhao, National Mobile Communications Research Lab., Southeast University

A Multipath AoA/AoD-Based Shared Dictionary Learning Framework for FDD Massive MIMO Channe...

This paper addresses the compressive sensing (CS)-based frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) downlink channel estimation problem in dynamic scenarios. We propose a multipath angle of arrival (AoA) and angle of departure (AoD)-based shared dictionary learning (MASDL) algorithm, where the discriminative and shared features in the angular domain are exploited via supervised dictionary learning, enhancing the generalization ability of the model. Simulation results show that the proposed algorithm achieves better normalized mean square error (NMSE) performance and substantially reduces the pilot overhead compared with other channel estimation schemes.A Multipath AoA/AoD-Based Shared Dictionary Learning Framework for FDD Massive MIMO Channel Estimationwenzhe fu, southeast university; Xinran Sun, Southeast university; Chunguo Li, Southeast University, Nanjing, China; Yongming Huang, Luxi Yang, Southeast University

Message Passing-Initialized Approximated-Variance Expectation Propagation (MPI-AVEP) Detec...

High-mobility communications in 6G systems face challenges due to Doppler shifts that degrade the performance of traditional orthogonal frequency division multiplexing (OFDM). Affine frequency division multiplexing (AFDM) offers high spectral efficiency but requires efficient detection to exploit its diversity gain. We introduce expectation propagation (EP) as an effective detector for AFDM, improving decoding by estimating the joint posterior distribution of symbols. However, EP incurs high complexity due to matrix inversion, and the Neumann-series approximation (NSA), which is widely used to reduce matrix inversion complexity, fails in AFDM systems due to ill-conditioned channel matrices. To address this, we propose a message passing-initialized approximated-variance EP (MPI-AVEP). It exploits the quasi-banded structure of AFDM channel matrices to reduce the total complexity. The MPI-AVEP achieves performance similar to traditional EP while reducing complexity from cubic to linear in the number of subcarriers. Thus, it offers a practical solution for high-mobility communication systems.Message Passing-Initialized Approximated-Variance Expectation Propagation (MPI-AVEP) Detector for AFDM WaveformShao-Chun Wang, Chi-Wei Chen, Yi-Ming Lee, An-Yeu (Andy) Wu, National Taiwan University

Message passing-based sparse spatial channel estimation robust to partially coherent phas...

Channel estimation can lead to a substantial training overhead in millimeter wave (mmWave) and terahertz (THz) systems employing large arrays. Prior work has leveraged channel sparsity at these frequencies to reduce this overhead. Most of the sparsity-aware algorithms, however, assume perfect phase coherence in the channel measurements, which is disrupted due to phase noise. Due to the errors induced by phase noise, standard sparse channel estimation algorithms assuming perfect phase coherence can fail. In this paper, we consider a frame structure in which the channel measurements are acquired over multiple packets. Our model assumes that the phase errors remain constant within a packet and vary considerably across different packets, leading to partially coherent channel measurements. We develop a message passing-based technique for sparse channel estimation under such partially coherent phase errors and show that our approach achieves a lower channel reconstruction error than comparable benchmarks.Message passing-based sparse spatial channel estimation robust to partially coherent phase noiseHamed Masoumi, Nitin Jonathan Myers, Delft University of Technology

Message Passing Based Two-Timescale Bayesian Deep Learning for Joint Channel and Hardware ...

In time division duplex (TDD) massive MIMO systems, the multiple antenna BS suffers severe hardware impairments such as power amplifier nonlinearity, crosstalk and in-phase/quadrature (IQ) imbalance, which significantly degrade the uplink channel estimation performance, especially for hybrid analog-digital beamforming (HBF) systems. Though polynomial and neural network (NN) have been proposed to compensate for the impairments, there are few works consider online joint channel and hardware impairments estimation at the receiver based on pilot signals, which is necessary when impairments varies slowly over time, leading to model mismatch. In this work, we model the impairments using a ResNet and propose a two-timescale Bayesian framework for joint short-term channel tracking and long-term hardware impairments estimation, where the parameters both in the channel model and the ResNet follow a time-evolving Markov prior, while the transition probabilities are set at different timescales. To obtain the marginal posterior distributions of the parameters, we propose a message passing-based two-timescale Bayesian deep learning (MP-TTBDL) algorithm: the messages w.r.t. the channel are computed via turbo orthogonal approximate message passing (Turbo-OAMP), while the messages within the ResNet are computed via deep approximate message passing (DAMP). Such a two-timescale joint estimation scheme can better track the fast-varying wireless channel and slow-varying impairments. Simulations show that the proposed scheme outperforms various baseline schemes under practical scenarios when the impairments and the channel vary at different timescales.Message Passing Based Two-Timescale Bayesian Deep Learning for Joint Channel and Hardware Impairments EstimationWei Xu, Zhejiang University; An Liu, College of ISEE, Zhejiang University

Advanced Channel Decomposition Techniques in OTFS: A GSVD Approach for Multi-User Downlink

In this paper, we propose a multi-user downlink system for two users based on the orthogonal time frequency space (OTFS) modulation scheme. The design leverages the generalized singular value decomposition (GSVD) of the channels between the base station and the two users, applying precoding and detection matrices based on the right and left singular vectors, respectively. We derive the analytical expressions for three scenarios and present the corresponding simulation results. These results demonstrate that, in terms of bit error rate (BER), the proposed system outperforms the conventional multi-user OTFS system in two scenarios when using minimum mean square error (MMSE) equalizers or precoder, both for perfect channel state information and for a scenario with channel estimation errors. In the third scenario, the design is equivalent to zero-forcing (ZF) precoding at the transmitter.Advanced Channel Decomposition Techniques in OTFS: A GSVD Approach for Multi-User DownlinkOmid Abbassi Aghda, NOVA University of Lisbon; Oussama Ben Haj Belkacem, )Instituto de Telecomunicacoes, Lisboa, Portugal.; Dou Hu, University of Tokyo; João Guerreiro, FCT-Universidade Nova de Lisboa, Instituto de Telecomunicações; Nuno Souto, Instituto de Telecomunicações/ISCTE-IUL; Michal Szczachor, NOKIA; Rui Dinis, Universidade Nova de Lisboa

A Robust Frequency MMSE Estimator Based on the Weighted Average of Multipath Signal for OT...

A robust frequency minimum mean square error (MMSE) estimator based on the weighted average of multipath signal for OTFS systems is proposed in this paper. Previous frequency estimators are based on the strongest single path signal or equally weighted average of multiple path signals so that the accuracies of estimators are limited. We generalise the weight coefficients to be taken arbitrarily, and their optimal values are determined such that the MSE of estimator is minimum. Simulation results show that the proposed method outperforms existing frequency estimators in terms of MSE performance, particularly when the normalized carrier frequency offset (CFO) is less than 0.5 and the symbol signal-to-noise ratio (SNR) of received signal is below 2 dB. Under static channel conditions, the proposed estimator can achieve a reduction in pilot energy of approximately 21.6 dB.A Robust Frequency MMSE Estimator Based on the Weighted Average of Multipath Signal for OTFS SystemsKe Zhou, Southeast University; Yishan He, China Satellite Network Network Exploration Co., Ltd; Chen Ming, Jie Wang, Jiaying Zhu, Nengtang Hua, Huilin Song, Southeast University

Quasi-Optimum Detection of OFDM with Cartesian Nonlinearities

In the last years, it has been shown that nonlinear (NL) orthogonal frequency division multiplexing (OFDM) can outperform linear OFDM because the nonlinear distortion has useful information about the transmitted signals. However, only a maximum likelihood (ML) receiver can exploit this information. Despite its potential, the optimal ML receiver for NL OFDM is highly complex, and its performance is difficult to simulate. Moreover, although some theoretical performance bounds exist for OFDM transmissions involving many subcarriers and high signal-to-noise ratio (SNR), the behavior of NL OFDM under more practical SNR conditions remains insufficiently explored. This paper focuses on optimal detection methods for NL OFDM systems. We present a comprehensive analysis of how the distortion received on different subcarriers contributes to the signal of a specific subcarrier and derive a performance bound for the ML detection of NL OFDM that is applicable across a broad range of SNR values. Furthermore, we propose a practical iterative decision-directed receiver that achieves significant performance improvements over linear OFDM in both uncoded and coded setups.Quasi-Optimum Detection of OFDM with Cartesian NonlinearitiesDaniel Dinis, IST-Universidade de Lisboa, Aalto University, Copelabs; Diogo Costa, IST, Instituto Superior Técnico; Aalto University; João Guerreiro, FCT-Universidade Nova de Lisboa, Instituto de Telecomunicações; Marko Beko, Instituto Superior Técnico, Universidade de Lisboa/COPELABS; Risto Wichman, Aalto University

On Optimizing the CP length for MISO-OFDM in 6G Industrial Networks

Current orthogonal frequency division multiplexing (OFDM) standards specify limited options for cyclic prefix (CP) duration, regardless of the wireless channel characteristics. These fixed options can result in significant overhead when the channel delay spread is very short. To address this, a more flexible approach to CP selection is needed, allowing for CP lengths that may be shorter than the delay spread. In this paper, we revisit the classical issue of waveform optimization for channels with short delay spreads, and investigate the potential to reduce the CP duration in OFDM. Building on our prior work in [1], we extend the analysis to multi-antenna systems and assess the impact of number of antennas on multiple-input single-output (MISO)-OFDM system with reduced CP durations. We first derive closed-form expressions for the desired signal power and inter-symbol interference (ISI) power in MISO-OFDM where the CP duration is shorter than the length of channel impulse response (CIR). Then, conditioned on the radio link reliability, defined by the link outage probability, we formulate an optimization problem to jointly determine the minimum CP duration and SNR values required for the system to satisfy that reliability condition. To solve the optimization problem, we use a weighted-sum approach combined with the Bisection method. Our analysis demonstrates that energy efficiency comparable to conventional OFDM systems can be maintained, while achieving increased spectral efficiency (SE) due to the reduced CP duration.On Optimizing the CP length for MISO-OFDM in 6G Industrial NetworksMohammad Parvini, Technische Universität Dresden; Muhammad Qurratulain Khan, TU Dresden; Ahmad Nimr, Technische Universität Dresden; Gerhard Fettweis, TU Dresden