Worst-Case Robust Training Design for Correlated MIMO Channels in the Presence of Colored Interference

Jae-Mo Kang and Sangseok Yun ()
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Jae-Mo Kang: Department of Artificial Intelligence, Kyungpook National University, Daegu 41566, Republic of Korea
Sangseok Yun: Department of Information and Communications Engineering, Pukyong National University, Busan 48513, Republic of Korea

Mathematics, 2025, vol. 13, issue 13, 1-24

Abstract: The covariance information at the transmitter side is often subject to mismatches due to various impairments. This paper considers a training design problem for multiple-input multiple-output (MIMO) systems when both channel and interference covariance matrices are imperfect at the transmitter side. We first derive the structure of the optimal training signal, minimizing the worst-case mean square error (MSE). With the training structure, the original problem becomes a simple power allocation problem. We propose a numerical optimal power allocation scheme and a closed-form suboptimal power allocation scheme. Simulation results show that the proposed schemes considerably outperform the conventional schemes in terms of the worst-case MSE and bit error rate (BER) performances, and the proposed closed-form training scheme has comparable performance to that of the optimal one. For example, the proposed schemes yield more than 2.5 dB signal-to-interference ratio (SIR) gains at a BER of 10 − 4 .

Keywords: imperfect covariance matrix; MIMO channel estimation; minimax approach; robust training optimization; worst-case robustness (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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