Robust Symbol Detection in Large-Scale Overloaded NOMA Systems
We a framework for the design of low-complexity and high-performance receivers for multidimensional overloaded non-orthogonal multiple access (NOMA) systems. The framework is built upon a novel compressed sensing (CS) regularized maximum likelihood (ML) formulation of the discrete-input detection pr...
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| Format: | Article |
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IEEE
2021-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/9373674/ |
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| author | Hiroki Iimori Giuseppe Thadeu Freitas De Abreu Takanori Hara Koji Ishibashi Razvan-Andrei Stoica David Gonzalez G. Osvaldo Gonsa |
| author_facet | Hiroki Iimori Giuseppe Thadeu Freitas De Abreu Takanori Hara Koji Ishibashi Razvan-Andrei Stoica David Gonzalez G. Osvaldo Gonsa |
| author_sort | Hiroki Iimori |
| collection | DOAJ |
| description | We a framework for the design of low-complexity and high-performance receivers for multidimensional overloaded non-orthogonal multiple access (NOMA) systems. The framework is built upon a novel compressed sensing (CS) regularized maximum likelihood (ML) formulation of the discrete-input detection problem, in which the <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-norm is introduced to enforce adherence of the solution to the prescribed discrete symbol constellation. Unlike much of preceding literature,.g., (Assaf <italic>et al.</italic>, 2020, Yeom <italic>et al.</italic>, 2019, Nagahara, 2015, Naderpour and Bizaki, 2020, Hayakawa and Hayashi, 2017, Hayakawa and Hayashi, 2018, and Zeng <italic>et al.</italic>, 2020), the method is not relaxed into the <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-norm, but rather approximated with a continuous and asymptotically exact expression without resorting to parallel interference cancellation (PIC). The objective function of the resulting formulation is thus a sum of concave-over-convex ratios, which is then tightly convexified via the quadratic transform (QT), such that its solution can be obtained via the iteration of a simple closed-form expression that closely resembles that of the classic zero-forcing (ZF) receiver, making the method particularly suitable to large-scale set-ups. By further transforming the aforementioned problem into a quadratically constrained quadratic program with one convex constraint (QCQP-1), the optimal regularization parameter to be used at each step of the iterative algorithm is then shown to be the largest generalized eigenvalue of a pair of matrices which are given in closed-form. The method so obtained, referred to as the Iterative Discrete Least Square (IDLS), is then extended to address several factors of practical relevance, such as noisy conditions, imperfect channel state information (CSI), and hardware impairments, thus yielding the Robust IDLS algorithm. Simulation results show that the proposed art significantly outperforms both classic receivers, such as the linear minimum mean square error (LMMSE), and recent CS-based state-of-the-art (SotA) alternatives, such as the sum-of-absolute-values (SOAV) and the sum of complex sparse regularizers (SCSR) detectors. It is also shown via simulations that the technique can be integrated with existing iterative detection-and-decoding (IDD) methods, resulting in accelerated convergence. |
| first_indexed | 2024-12-19T13:55:08Z |
| format | Article |
| id | doaj.art-c82581ddf70e4c76b48ea347300cb09a |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-12-19T13:55:08Z |
| publishDate | 2021-01-01 |
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| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-c82581ddf70e4c76b48ea347300cb09a2022-12-21T20:18:37ZengIEEEIEEE Open Journal of the Communications Society2644-125X2021-01-01251253310.1109/OJCOMS.2021.30649839373674Robust Symbol Detection in Large-Scale Overloaded NOMA SystemsHiroki Iimori0https://orcid.org/0000-0003-3417-1513Giuseppe Thadeu Freitas De Abreu1https://orcid.org/0000-0002-5018-8174Takanori Hara2https://orcid.org/0000-0001-5647-2638Koji Ishibashi3https://orcid.org/0000-0002-7145-5622Razvan-Andrei Stoica4https://orcid.org/0000-0003-3801-0055David Gonzalez G.5https://orcid.org/0000-0003-2090-8481Osvaldo Gonsa6https://orcid.org/0000-0001-5452-8159Department of Electrical and Computer Engineering, Jacobs University Bremen, Bremen, GermanyDepartment of Electrical and Computer Engineering, Jacobs University Bremen, Bremen, GermanyAdvanced Wireless and Communication Research Center, The University of Electro-Communications, Tokyo, JapanAdvanced Wireless and Communication Research Center, The University of Electro-Communications, Tokyo, JapanWireless Communications R&D, WIOQnet GmbH, Bremen, GermanyAdvanced Connectivity Technologies Group, Continental AG, Frankfurt, GermanyAdvanced Connectivity Technologies Group, Continental AG, Frankfurt, GermanyWe a framework for the design of low-complexity and high-performance receivers for multidimensional overloaded non-orthogonal multiple access (NOMA) systems. The framework is built upon a novel compressed sensing (CS) regularized maximum likelihood (ML) formulation of the discrete-input detection problem, in which the <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-norm is introduced to enforce adherence of the solution to the prescribed discrete symbol constellation. Unlike much of preceding literature,.g., (Assaf <italic>et al.</italic>, 2020, Yeom <italic>et al.</italic>, 2019, Nagahara, 2015, Naderpour and Bizaki, 2020, Hayakawa and Hayashi, 2017, Hayakawa and Hayashi, 2018, and Zeng <italic>et al.</italic>, 2020), the method is not relaxed into the <inline-formula> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula>-norm, but rather approximated with a continuous and asymptotically exact expression without resorting to parallel interference cancellation (PIC). The objective function of the resulting formulation is thus a sum of concave-over-convex ratios, which is then tightly convexified via the quadratic transform (QT), such that its solution can be obtained via the iteration of a simple closed-form expression that closely resembles that of the classic zero-forcing (ZF) receiver, making the method particularly suitable to large-scale set-ups. By further transforming the aforementioned problem into a quadratically constrained quadratic program with one convex constraint (QCQP-1), the optimal regularization parameter to be used at each step of the iterative algorithm is then shown to be the largest generalized eigenvalue of a pair of matrices which are given in closed-form. The method so obtained, referred to as the Iterative Discrete Least Square (IDLS), is then extended to address several factors of practical relevance, such as noisy conditions, imperfect channel state information (CSI), and hardware impairments, thus yielding the Robust IDLS algorithm. Simulation results show that the proposed art significantly outperforms both classic receivers, such as the linear minimum mean square error (LMMSE), and recent CS-based state-of-the-art (SotA) alternatives, such as the sum-of-absolute-values (SOAV) and the sum of complex sparse regularizers (SCSR) detectors. It is also shown via simulations that the technique can be integrated with existing iterative detection-and-decoding (IDD) methods, resulting in accelerated convergence.https://ieeexplore.ieee.org/document/9373674/Non-orthgonal multi-access (NOMA)detectionoverloaded systemsfractional programmingiterative least square |
| spellingShingle | Hiroki Iimori Giuseppe Thadeu Freitas De Abreu Takanori Hara Koji Ishibashi Razvan-Andrei Stoica David Gonzalez G. Osvaldo Gonsa Robust Symbol Detection in Large-Scale Overloaded NOMA Systems IEEE Open Journal of the Communications Society Non-orthgonal multi-access (NOMA) detection overloaded systems fractional programming iterative least square |
| title | Robust Symbol Detection in Large-Scale Overloaded NOMA Systems |
| title_full | Robust Symbol Detection in Large-Scale Overloaded NOMA Systems |
| title_fullStr | Robust Symbol Detection in Large-Scale Overloaded NOMA Systems |
| title_full_unstemmed | Robust Symbol Detection in Large-Scale Overloaded NOMA Systems |
| title_short | Robust Symbol Detection in Large-Scale Overloaded NOMA Systems |
| title_sort | robust symbol detection in large scale overloaded noma systems |
| topic | Non-orthgonal multi-access (NOMA) detection overloaded systems fractional programming iterative least square |
| url | https://ieeexplore.ieee.org/document/9373674/ |
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