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<p>Reconfigurable intelligent surface (RIS)-aided Multiple Input and Multiple Output (MIMO) systems offer increased spectral efficiency and signal quality in futuristic wireless networks. However, optimizing channel equalization in time-varying channel conditions and fronthaul-constrained capacity is a challenging task. This paper introduces an in-context learning (ICL)-based equalization scheme that can adapt to changing channel conditions in real time without the need for retraining. Compared to conventional methods like linear minimum mean square error (LMMSE) and model-agnostic meta-learning (MAML), ICL is unique as it uses a transformer model to learn optimal equalization policies from pilot data. This makes it efficient and computationally less complex. Most RIS-aided cell-free MIMO (CF-MIMO) network simulations show that ICL reduces mean squared error (MSE) by up to <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(80\%\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>80</mn> <mo>%</mo> </mrow> </math></EquationSource> </InlineEquation> and improves spectral efficiency and capacity with limited fronthaul capacity (b). The results validate the scalability and performance of ICL and make it a promising candidate for deployment in low-resource wireless systems. Our future efforts are focused on extending ICL to multi-user and RIS environments, optimizing equalization with reinforcement learning, and confirming its performance in real-world hardware environments.</p>

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Improving channel equalization in cell-free MIMO networks using reconfigurable intelligent surfaces and in-context learning

  • 期刊论文

  • 发表日期：

    2025年7月25日

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  • Manju Shanmugam,
  • Sundarambal Balaraman,
  • Krishnamoorthy Ranganathan

摘要

Reconfigurable intelligent surface (RIS)-aided Multiple Input and Multiple Output (MIMO) systems offer increased spectral efficiency and signal quality in futuristic wireless networks. However, optimizing channel equalization in time-varying channel conditions and fronthaul-constrained capacity is a challenging task. This paper introduces an in-context learning (ICL)-based equalization scheme that can adapt to changing channel conditions in real time without the need for retraining. Compared to conventional methods like linear minimum mean square error (LMMSE) and model-agnostic meta-learning (MAML), ICL is unique as it uses a transformer model to learn optimal equalization policies from pilot data. This makes it efficient and computationally less complex. Most RIS-aided cell-free MIMO (CF-MIMO) network simulations show that ICL reduces mean squared error (MSE) by up to \(80\%\) 80 % and improves spectral efficiency and capacity with limited fronthaul capacity (b). The results validate the scalability and performance of ICL and make it a promising candidate for deployment in low-resource wireless systems. Our future efforts are focused on extending ICL to multi-user and RIS environments, optimizing equalization with reinforcement learning, and confirming its performance in real-world hardware environments.