SmartUT: Receive Beamforming for Spectral Coexistence of NGSO Satellite Systems

• URL: http://arxiv.org/abs/2505.07714v2
• Date: Thu, 09 Oct 2025 09:26:26 GMT
• Title: SmartUT: Receive Beamforming for Spectral Coexistence of NGSO Satellite Systems
• Authors: Almoatssimbillah Saifaldawla, Eva Lagunas, Flor Ortiz, Abuzar B. M. Adam, Symeon Chatzinotas,
• Abstract summary: We investigate downlink co-frequency interference (CFI) mitigation in non-geostationary satellites orbits (NGSOs) co-existing systems.<n>Traditional mitigation techniques, such as Zero-forcing (ZF), produce a null towards the direction of arrivals (DOAs) of the interfering signals.<n>We propose a Mamba-based beamformer (MambaBF) that leverages an unsupervised deep learning (DL) approach and can be deployed on the user terminal (UT) antenna array.
• Score: 42.72167382331346
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we investigate downlink co-frequency interference (CFI) mitigation in non-geostationary satellites orbits (NGSOs) co-existing systems. Traditional mitigation techniques, such as Zero-forcing (ZF), produce a null towards the direction of arrivals (DOAs) of the interfering signals, but they suffer from high computational complexity due to matrix inversions and required knowledge of the channel state information (CSI). Furthermore, adaptive beamformers, such as sample matrix inversion (SMI)-based minimum variance, provide poor performance when the available snapshots are limited. We propose a Mamba-based beamformer (MambaBF) that leverages an unsupervised deep learning (DL) approach and can be deployed on the user terminal (UT) antenna array, for assisting downlink beamforming and CFI mitigation using only a limited number of available array snapshots as input, and without CSI knowledge. Simulation results demonstrate that MambaBF consistently outperforms conventional beamforming techniques in mitigating interference and maximizing the signal-to-interference-plus-noise ratio (SINR), particularly under challenging conditions characterized by low SINR, limited snapshots, and imperfect CSI.

Related papers

• Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC Under Channel Uncertainty [36.06255760148067]
  Integrated sensing and communication (ISAC) systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication.<n>This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel state information (CSI) and in the presence of unknown eavesdropper (Eve) locations.
  arXiv  Detail & Related papers  (2026-03-05T11:20:27Z)
• Learning to Separate RF Signals Under Uncertainty: Detect-Then-Separate vs. Unified Joint Models [53.79667447811139]
  We show that a single deep neural architecture learns to jointly detect and separate when applied directly to the received signal.<n>These findings highlight UJM as a scalable and practical alternative to DTS, while opening new directions for unified separation under broader estimation.
  arXiv  Detail & Related papers  (2026-02-04T15:25:02Z)
• PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition [49.955269674859004]
  This paper introduces PhyG-MoE (Physics-Guided Mixture-of-Experts), a framework designed to align model capacity with signal complexity.<n>Unlike static architectures, the proposed system employs a spectrum-based gating mechanism that routes signals based on their spectral feature entanglement.<n>A high-capacity TransNeXt expert is activated on-demand to disentangle complex features in saturated scenarios, while lightweight experts handle fundamental signals to minimize latency.
  arXiv  Detail & Related papers  (2026-01-19T07:57:52Z)
• AmbShield: Enhancing Physical Layer Security with Ambient Backscatter Devices against Eavesdroppers [69.56534335936534]
  AmbShield is an AmBD-assisted PLS scheme that leverages naturally distributed AmBDs to simultaneously strengthen the legitimate channel and degrade eavesdroppers'<n>In AmbShield, AmBDs are exploited as friendly jammers that randomly backscatter to create interference at eavesdroppers, and as passive relays that backscatter the desired signal to enhance the capacity of legitimate devices.
  arXiv  Detail & Related papers  (2026-01-14T20:56:50Z)
• Neural Beamforming with Doppler-Aware Sparse Attention for High Mobility Environments [35.90167049594104]
  Deep learning-based beamforming offers an alternative with nonlinear mappings from channel state information to beamforming weights.<n> Transformer-based models are particularly effective due to their ability to model long-range dependencies across time and frequency.<n>Recent studies address this issue through sparse attention mechanisms that reduce complexity while maintaining, yet often employ patterns that disregard channel dynamics.
  arXiv  Detail & Related papers  (2025-11-05T16:51:26Z)
• Robust Super-Capacity SRS Channel Inpainting via Diffusion Models [19.333344273742014]
  In 5G NR beamforming, reciprocity-based beamforming via uplink Sounding Reference Signals (SRS) face resource and coverage constraints.<n>We propose a diffusion-based channel inpainting framework that integrates system-model knowledge at inference.<n>We show that the score-based diffusion variant consistently outperforms a UNet score-model baseline and the one-step MAE under distribution shift.
  arXiv  Detail & Related papers  (2025-10-30T03:22:13Z)
• Adaptive Learning for IRS-Assisted Wireless Networks: Securing Opportunistic Communications Against Byzantine Eavesdroppers [7.256056777973974]
  We propose a joint learning framework for Byzantine-resilient spectrum sensing and secure intelligent reflecting surface (IRS)<n>We develop an augmented-Lagrangian alternating algorithm with projected updates and provide provable sublinear convergence, with accelerated rates under mild local curvature.<n> Simulations across diverse network conditions show higher detection probability at fixed false-alarm rate under adversarial attacks, large reductions in sum MSE for honest users, strong suppression of eavesdropper signal power, and fast convergence.
  arXiv  Detail & Related papers  (2025-08-11T17:28:25Z)
• Transformer-Driven Neural Beamforming with Imperfect CSI in Urban Macro Wireless Channels [27.445841110148674]
  We introduce a novel unsupervised deep learning framework that integrates depthwise separable convolutions and transformers to generate beamforming weights under imperfect channel state information.<n>The primary goal is to enhance throughput by maximizing sum-rate while ensuring reliable communication.
  arXiv  Detail & Related papers  (2025-04-15T23:41:24Z)
• Sparse Hyperparametric Itakura-Saito NMF via Bi-Level Optimization [1.5379084885764847]
  We propose a new algorithm called SHINBO, which introduces a bi-level optimization framework to automatically and adaptively tune the row-dependent penalty hyper parameters.<n> Experimental results showed SHINBO ensures precise spectral decomposition and demonstrates superior performance in both synthetic and real-world applications.
  arXiv  Detail & Related papers  (2025-02-24T13:05:01Z)
• Generalizable Non-Line-of-Sight Imaging with Learnable Physical Priors [52.195637608631955]
  Non-line-of-sight (NLOS) imaging has attracted increasing attention due to its potential applications. Existing NLOS reconstruction approaches are constrained by the reliance on empirical physical priors. We introduce a novel learning-based solution, comprising two key designs: Learnable Path Compensation (LPC) and Adaptive Phasor Field (APF)
  arXiv  Detail & Related papers  (2024-09-21T04:39:45Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications [68.96633803796003]
  This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration. In this paper, we conceive an MU-LMA system employing a full-array structured (FAS) transmitter and propose two algorithms accordingly.
  arXiv  Detail & Related papers  (2023-11-08T08:54:56Z)
• Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI [14.04355073946466]
  A deep autoencoder (DAE)-based structure for endto-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The proposed structure jointly optimize the two encoder/decoder pairs and generates interference-aware constellations that dynamically adapt their shape based on interference intensity to minimize the bit error rate (BER) An in-phase/quadrature-phase (I/Q) power allocation layer is introduced in the DAE to guarantee an average power constraint and enable the architecture to generate constellations with nonuniform shapes.
  arXiv  Detail & Related papers  (2023-10-23T15:23:42Z)
• Non-Coherent Over-the-Air Decentralized Gradient Descent [0.0]
  Implementing Decentralized Gradient Descent in wireless systems is challenging due to noise, fading, and limited bandwidth. This paper introduces a scalable DGD algorithm that eliminates the need for scheduling, topology information, or CSI.
  arXiv  Detail & Related papers  (2022-11-19T19:15:34Z)
• Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks [101.59367762974371]
  In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments. It is difficult to train a deep neural network (DNN) with limited pilot signals, hindering its practical applications. We design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm.
  arXiv  Detail & Related papers  (2022-10-08T04:32:58Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming [45.257328085051974]
  Wireless communications and sensing at terahertz (THz) band are investigated as promising short-range technologies. Ultra-massive multiple-input multiple-output (UM-MIMO) antenna systems have been proposed for THz communications to compensate propagation losses. We develop THz hybrid beamformers based on both model-based and model-free techniques for a new group-of-subarrays (GoSA) UM-MIMO structure.
  arXiv  Detail & Related papers  (2021-02-27T21:28:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.