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Improved QRD-QLD Algorithm for Low Complexity mimo decoding

IEEE COMMUNICATIONS LETTERS 2014年第10期18卷 1703-1706页

作者： Peer, Nimrod Murin, Yonathan Dabora, Ron Ben Gurion Univ Negev Dept Elect & Comp Engn IL-84105 Beer Sheva Israel

In this paper we present a decoding algorithm for multiple-input multiple-output (mimo) communications. The algorithm is based on the QRD-QLD algorithm (see, e. g., Radosavljevic et al. 2012), proposes an improved path metric computation, and achieves a low probability of error which is close to the single tree search sphere decoder, at low implementation complexity. The proposed improved path metric computation increases the reliability of combining the results of the partial searches into sequences representing the maximum likelihood (ML) and the counter ML hypotheses in soft input mimo decoding. The excellent performance of the improved algorithm are demonstrated via numerical simulations.

关键词： mimo decoding QR-QL decomposition

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Parallel decoding for lattice reduction-aided mimo Receiver

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第6期24卷 5173-5182页

作者： Karthikeyan, Madurakavi Saraswady, Djagadeesan Pondicherry Engn Coll Dept Elect & Commun Pondicherry India

In this paper, we propose a modified lattice reduction (LR) for parallel decoding (PD) in multiple input and multiple output (mimo) systems. Compared with the conventional methods, without any performance loss,the symbols can be estimated in parallel using this LR-PD algorithm. This parallel decoding is achieved through the proposed postrotation matrix. Among the various LR algorithms, LLL has been considered in the analysis of this postrotation matrix. However, this can be applied to any other LR algorithms without any modifications. In simulations, we show that with a minimal number of extra arithmetic operations and without any significant loss in orthogonal defect and bit error rate performance, the parallel decoding becomes possible in a real as well as complex-valued mimo system mode.

关键词： mimo decoding LR-PD LLL lattice reduction parallel decoding

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A hybrid Zero-Forcing and Sphere-decoding method for mimo systems

A hybrid Zero-Forcing and Sphere-Decoding method for MIMO sy...

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2nd IEEE International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2006)

作者： Lee, Hong-Ju Kim, Dongwoo Hanyang Univ Dept Elect Engn Ansan 425791 South Korea

ISBN: (纸本)9781424405169

In this paper, we describe a hybrid ZF-SD (Zero-Forcing and Sphere decoding) algorithm. The basic idea is to reduce the dimensionality of decoding via pre-decoding and cancellation of those symbols that can be quickly and reliably detected. The proposed algorithm shows the BER performance similar to SD (Sphere decoding) but with very lower computational complexity than SD.

关键词： mimo decoding sphere decoding zero-forcing decoding hybrid ZF-SD

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A Hybrid Zero-Forcing and Sphere-decoding Method for mimo Systems

A Hybrid Zero-Forcing and Sphere-Decoding Method for MIMO Sy...

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2nd International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2006), vol.1

作者： Hong-Ju Lee Dongwoo Kim Department of Electrical Engineering Hanyang University South Korea

关键词： mimo decoding Sphere decoding Zero-forcing decoding Hybrid ZF-SD

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Low complexity layered tabu search detection in large mimo systems

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2018年 83卷 106-113页

作者： Karthikeyan, Madurakavi Saraswady, Djagadeesan Madanapalle Inst Technol & Sci Dept ECE Madanapalle 517325 Andhra Prades India Pondicherry Engn Coll Dept ECE Pondicherry 605014 India

In this paper, a layered tabu search (LTS) algorithm for a complex-valued channel matrix (LTS-C) is developed to provide optimal detection in large mimo systems. The proposed LTS-C algorithm works directly on the complex-valued channel matrix rather than converting it to a real-valued matrix. The contribution of this paper is twofold. First, we propose a novel neighborhood approach based on a signal constellation graph to obtain a set of neighborhoods for the given complex-valued initial vector. Second, the tabu matrix structure is changed to track complex-valued moves. The proposed structure significantly reduces the size of the tabu matrix. Our simulation results show that the complexity reduction is substantial in the LTS-C algorithm over the LTS algorithm. In particular, LTS-C is able to achieve approximately 45-50% and 93-94% of complexity reduction in terms of the number of real operations for 16 x 16 and 32 x 32 mimo systems, respectively. (C) 2017 Elsevier GmbH. All rights reserved.

关键词： LTS-C Large mimo Tabu search mimo decoding RTS-C LTS RTS

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Deep Learning Based Massive-mimo Decoder 13

Deep Learning Based Massive-MIMO Decoder

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13th IEEE International Conference on Advanced Networks and Telecommunication Systems (IEEE ANTS)

作者： Kumar, Satish Singh, Anurag Mahapatra, Rajarshi Dr SPM Int Inst Informat Technol Naya Raipur Elect & Commun Engn Dept Atal Nagar CG India

ISBN: (纸本)9781728137155

Massive Multiple Input Multiple Output (M-mimo) is one of the key technology for 5th generation (5G) communication systems. Designing of M-mimo decoder is very complex and challenging task. Recently, deep neural network (DNN) emerges as an alternative way to perform many complex tasks. In this work we have presented a deep learning (DL) based architecture (DLNet) for receive signal decoding in Massive-mimo system. In this we have considered an uplink time-varying Gaussian random mimo channel perfectly known to the receiver. Based on the knowledge of this mimo channel and the received signal the proposed DLNet decoder is decoding the messages of all the users. The presented decoder is 50 layer deep neural network. Its architecture is based on the projected gradient descent algorithm. Our simulation result shows, the proposed DLNet decoder for M-mimo system performs better than other mimo decoding techniques, by 3 dB bit error rate (BER), atleast 124 times faster and 9 times less complex.

关键词： mimo decoding Deep Neural Network Deep Learning Massive mimo Projected Gradient Descent

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A parallel hybrid merge-select sorting scheme for K-best LSD mimo decoder on a dynamically reconfigurable processor

A parallel hybrid merge-select sorting scheme for K-best LSD...

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21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Wang, Zong Erdogan, Ahmet T. Arslan, Tughrul Univ Edinburgh Sch Engn Edinburgh EH9 3JL Midlothian Scotland

ISBN: (纸本)9781424480166

In this paper, we propose a parallel hybrid merge-select sorting approach for the implementation of K-best list sphere detection (LSD) multi-input multi-output (mimo) decoder based on a recently developed novel Reconfigurable Instruction Cell Array (RICA). Several popular sorting algorithms adopted in mimo decoding are analyzed and mapped onto our proposed platform. We discuss the targeted K-best LSD algorithm as well as the sorting scheme variations which have been tailored for our RICA architecture. Simulation results prove that our proposed hybrid sorting approach can significantly reduce the number of comparison and swap operations when selecting the K-best candidates. Our results show that a 50% speedup can be achieved compared to traditional single bubble sorting based K-best LSD mimo decoder.

关键词： K-best LSD mimo decoder mimo communication mimo decoding RICA decoding dynamically reconfigurable processor list sphere detection merging multiple-input multiple-output decoder parallel architectures parallel hybrid merge-select sorting scheme reconfigurable architectures reconfigurable instruction cell array signal detection software radio sorting

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DLNet: Deep learning-aided massive mimo decoder

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2022年 155卷

作者： Kumar, Satish Singh, Anurag Mahapatra, Rajarshi Dr SPM Int Inst Informat Technol Naya Raipur Dept Elect & Commun Engn Nava Raipur CG India

Traditional mimo decoding schemes are complex, impractical, and perform poorly for massive multipleinput multiple-output (M-mimo) systems. Deep learning (DL) has recently emerged to perform many complex operations more efficiently within a shorter time. This paper proposes a learning-based network (DLNet) to design an M-mimo decoder. The DLNet network architecture is designed by iteratively unfolding the gradient descent algorithm. The proposed DLNet decoder consists of 15 neural networks (NN) layers with some trainable parameters. This work considered uplink Rayleigh and correlated M-mimo channels, which are perfectly known to the receiver. With the knowledge of the received signals and the M-mimo channels, the proposed DLNet decoder decodes the messages of all the users. In the M-mimo perspective, the proposed DLNet has been evaluated for symbol-error-rate (SER) performance, algorithm complexity, and run-time requirement. The simulations show that the proposed DLNet converges faster than other available decoders and performs better than other M-mimo decoding schemes, by at least 2 dB in SER and at least 11 times faster than the baseline (OAMP-Net) and nine times less complex.

关键词： Correlated channel Deep learning Iterative network Massive mimo mimo decoding Projected gradient descent

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Complex-Valued Phase Transmittance RBF Neural Networks for Massive mimo-OFDM Receivers

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SENSORS 2021年第24期21卷 8200-8200页

作者： Soares, Jonathan Aguiar Mayer, Kayol Soares de Castro, Fernando Cesar Comparsi Arantes, Dalton Soares Univ Estadual Campinas Sch Elect & Comp Engn Dept Commun BR-13083852 Campinas Brazil Univ Fed Santa Maria Technol Ctr Dept Elect & Comp BR-97105900 Santa Maria RS Brazil

Multi-input multi-output (mimo) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for mimo channels remains a challenging task. The maximum likelihood detector can achieve excellent performance-usually, the best performance-but its computational complexity is a limiting factor in practical implementation. In the present work, a novel mimo scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for mimo-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.

关键词： artificial neural networks phase transmittance radial basis function massive mimo mimo decoding 5G

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