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1. Recovery analysis of damped spectrally sparse signals and its relation to MUSIC

   https://doi.org/10.1093/imaiai/iaaa024  

   Li, Shuang; Mansour, Hassan; Wakin, Michael B (October 2020, Information and Inference: A Journal of the IMA)

   null (Ed.)

   Abstract One of the classical approaches for estimating the frequencies and damping factors in a spectrally sparse signal is the MUltiple SIgnal Classification (MUSIC) algorithm, which exploits the low-rank structure of an autocorrelation matrix. Low-rank matrices have also received considerable attention recently in the context of optimization algorithms with partial observations, and nuclear norm minimization (NNM) has been widely used as a popular heuristic of rank minimization for low-rank matrix recovery problems. On the other hand, it has been shown that NNM can be viewed as a special case of atomic norm minimization (ANM), which has achieved great success in solving line spectrum estimation problems. However, as far as we know, the general ANM (not NNM) considered in many existing works can only handle frequency estimation in undamped sinusoids. In this work, we aim to fill this gap and deal with damped spectrally sparse signal recovery problems. In particular, inspired by the dual analysis used in ANM, we offer a novel optimization-based perspective on the classical MUSIC algorithm and propose an algorithm for spectral estimation that involves searching for the peaks of the dual polynomial corresponding to a certain NNM problem, and we show that this algorithm is in fact equivalent to MUSIC itself. Building on this connection, we also extend the classical MUSIC algorithm to the missing data case. We provide exact recovery guarantees for our proposed algorithms and quantify how the sample complexity depends on the true spectral parameters. In particular, we provide a parameter-specific recovery bound for low-rank matrix recovery of jointly sparse signals rather than use certain incoherence properties as in existing literature. Simulation results also indicate that the proposed algorithms significantly outperform some relevant existing methods (e.g., ANM) in frequency estimation of damped exponentials. 

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2. Channel Charting: Model-Based Approaches

   Aly, Amr: Ayanoglu. (June 2023, Proc. 2023 IEEE International Conference on Communications (ICC))

   We present new ways of producing a channel chart employing model-based approaches. We estimate the angle of arrival θ and the distance between the base station and the user equipment ρ by employing our algorithms, inverse of the root sum squares of channel coefficients (ISQ) algorithm, linear regression (LR) algorithm, and the MUSIC/MUSIC (MM) algorithm. We compare these methods with the channel charting algorithms principal component analysis (PCA), Sammon’s method (SM), and autoencoder (AE) from [1]. We show that ISQ, LR, and MM outperform all three in performance. ISQ and LR have similar performance with ISQ having less complexity than LR. The performance of MM is better than ISQ and LR but it is more complex. Finally, we introduce the rotate-and-sum (RS) algorithm which has about the same performance as the MM algorithm but is less complex due to the avoidance of the eigenvector and eigenvalue analysis and a potential register transfer logic (RTL) implementation. 

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3. Channel Charting: Model-Based Approaches

   Aly, Amr and (June 2023, Proc. 2023 IEEE International Conference on Communications (ICC))

   We present new ways of producing a channel chart employing model-based approaches. We estimate the angle of arrival θ and the distance between the base station and the user equipment ρ by employing our algorithms, inverse of the root sum squares of channel coefficients (ISQ) algorithm, linear regression (LR) algorithm, and the MUSIC/MUSIC (MM) algorithm. We compare these methods with the channel charting algorithms principal component analysis (PCA), Sammon’s method (SM), and autoencoder (AE) from [1]. We show that ISQ, LR, and MM outperform all three in performance. ISQ and LR have similar performance with ISQ having less complexity than LR. The performance of MM is better than ISQ and LR but it is more complex. Finally, we introduce the rotate-and-sum (RS) algorithm which has about the same performance as the MM algorithm but is less complex due to the avoidance of the eigenvector and eigenvalue analysis and a potential register transfer logic (RTL) implementation. 

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4. Experimental Results of Novel DoA Estimation Algorithms for Compact Reconfigurable Antennas

   https://doi.org/10.1155/2017/1613638  

   Paaso, Henna; Hakkarainen, Aki; Gulati, Nikhil; Patron, Damiano; Dandekar, Kapil R.; Valkama, Mikko; Mämmelä, Aarne (January 2017, International Journal of Antennas and Propagation)

   null (Ed.)

   Reconfigurable antenna systems have gained much attention for potential use in the next generation wireless systems. However, conventional direction-of-arrival (DoA) estimation algorithms for antenna arrays cannot be used directly in reconfigurable antennas due to different design of the antennas. In this paper, we present an adjacent pattern power ratio (APPR) algorithm for two-port composite right/left-handed (CRLH) reconfigurable leaky-wave antennas (LWAs). Additionally, we compare the performances of the APPR algorithm and LWA-based MUSIC algorithms. We study how the computational complexity and the performance of the algorithms depend on number of selected radiation patterns. In addition, we evaluate the performance of the APPR and MUSIC algorithms with numerical simulations as well as with real world indoor measurements having both line-of-sight and non-line-of-sight components. Our performance evaluations show that the DoA estimates are in a considerably good agreement with the real DoAs, especially with the APPR algorithm. In summary, the APPR and MUSIC algorithms for DoA estimation along with the planar and compact LWA layout can be a valuable solution to enhance the performance of the wireless communication in the next generation systems. 

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5. Performance Comparison of Four Recombining Methods in a Synthetic Diversity Single-Input Multi-Output Receiver

   Sadeghi, Sanaz; Zhai, Haotian; Molnar, Alyosha; Paris, Bernd-Peter (May 2025, IEEE)

   In this paper, we considered four different interference suppression algorithms in a single-input multiple-output receiver, where channel diversity is intentionally introduced to improve interference tolerance. Matched filter (MF), zero forcing (ZF), blind interference estimation and suppression (BIES) which we had previously proposed, and minimum variance distortionless response (MVDR) are considered. Each algorithm is introduced, and the recombining weight vectors are derived. A loss function is defined to compare the performance of the algorithms, showing superior performance of MVDR, and confirming that the proposed BIES algorithm achieves a comparable performance to MVDR. The four algorithms are then applied on measured data from a chip that was designed and fabricated in \qty{45}{\nm} RF SOI process for the frequency range of 1.2-2.4GHz. Measurement results are compared for the four algorithms, confirming significant improvement by using MVDR, BIES, and ZF compared to MF for large interference, as predicted by the derived equations, and showing adaptability of MVDR and BIES to small levels of interference as opposed to ZF. 

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