scholarly journals A Deep Neural Network-Based Interference Mitigation for MIMO-FBMC/OQAM Systems

2021 ◽  
Vol 2 ◽  
Author(s):  
Abla Bedoui ◽  
Mohamed Et-tolba
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Deep Neural Network ◽  
Interference Mitigation ◽  
Multiple Input Multiple Output ◽  
Filter Bank Multicarrier ◽  
Multiple Input ◽  
High Spectral Efficiency ◽  
Input Multiple Output ◽  
Imaginary Field

Offset quadrature amplitude modulation-based filter bank multicarrier (FBMC/OQAM) is among the promising waveforms for future wireless communication systems. This is due to its flexible spectrum usage and high spectral efficiency compared with the conventional multicarrier schemes. However, with OQAM modulation, the FBMC/OQAM signals are not orthogonal in the imaginary field. This causes a significant intrinsic interference, which is an obstacle to apply multiple input multiple output (MIMO) technology with FBMC/OQAM. In this paper, we propose a deep neural network (DNN)-based approach to deal with the imaginary interference, and enable the application of MIMO technique with FBMC/OQAM. We show, by simulations, that the proposed approach provides good performance in terms of bit error rate (BER).

Lattice Reduction Algorithm Based Uplink MC-CDMA

2015 ◽  
Vol 738-739 ◽  
pp. 391-396
Author(s):  
Umut Yunus ◽  
Askar Hamdulla ◽  
Zhen Hong Jia ◽  
Kurban Ubul
Keyword(s):  
Communication Systems ◽  
Multiple Access ◽  
Multiple Input Multiple Output ◽  
Lattice Reduction ◽  
Rate Performance ◽  
Multiple Input ◽  
High Spectral Efficiency ◽  
Input Multiple Output ◽  
Cdma Systems ◽  
Multi Access

MC-CDMA integrates the advantages of OFDM with those of CDMA, it has high spectral efficiency, robustness against multi-path propagation and multiple access flexibility. Due to the above mentioned merits, it has been considered as a candidate for future wireless. In recent years, lattice reduction technique is discussed in multiple input multiple output communication systems, and has been shown with its better performance. The purpose of this paper is to express a model for uplink MC-CDMA systems in matrix form and then to propose a lattice reduction aided multiuser detection, in order to ameliorate the affects of inter-carrier interference and multi access interference. The effectiveness of the proposed method is evaluated by the bit error rate performance.

PROBLEM STATEMENT OF JOINT ANTENNA SELECTION AND PRECODING IN MASSIVE MIMO COMMUNICATION SYSTEMS

2021 ◽  
Author(s):  
В.Б. КРЕЙНДЕЛИН ◽  
М.В. ГОЛУБЕВ
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Multiple Input Multiple Output ◽  
Research Areas ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Legal Restrictions ◽  
Multiple Transmission

Совместный с прекодингом автовыбор антенн на приемной и передающей стороне - одно из перспективных направлений исследований для реализации технологий Multiple Transmission and Reception Points (Multi-TRP, множество точек передачи и приема) в системах со многими передающими и приемными антеннами Massive MIMO (Multiple-Input-Multiple-Output), которые активно развиваются в стандарте 5G. Проанализированы законодательные ограничения, влияющие на применимость технологий Massive MIMO, и специфика реализации разрабатываемого алгоритма в миллиметровомдиапа -зоне длин волн. Рассмотрены алгоритмы формирования матриц автовыбора антенн как на передающей, так и на приемной стороне. Сформулирована строгая математическая постановка задачи для двух критериев работы алгоритма: максимизация взаимной информации и минимизация среднеквадратичной ошибки. Joint precoding and antenna selection both on transmitter and receiver sides is one of the promising research areas for evolving toward the Multiple Transmission and Reception Points (Multi-TRP) concept in Massive MIMO systems. This technology is under active development in the coming 5G 3GPP releases. We analyze legal restrictions for the implementation of 5G Massive MIMO technologies in Russia and the specifics of the implementation of the developed algorithm in the millimeter wavelength range. Algorithms of antenna auto-selection matrices formation on both transmitting and receiving sides are considered. Two criteria are used for joint antenna selection and precoding: maximizing mutual information and minimizing mean square error.

scholarly journals A New Subcarrier Allocation Strategy for MIMO-OFDMA Multicellular Networks Based on Cooperative Interference Mitigation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Panagiotis K. Gkonis ◽  
Maria A. Seimeni ◽  
Nikolaos P. Asimakis ◽  
Dimitra I. Kaklamani ◽  
Iakovos S. Venieris
Keyword(s):  
Multiple Access ◽  
Interference Mitigation ◽  
Traditional Approach ◽  
Multiple Input Multiple Output ◽  
Central Cell ◽  
Subcarrier Allocation ◽  
Allocation Strategy ◽  
Multiple Input ◽  
Qam Modulation ◽  
Input Multiple Output

The goal of the study presented in this paper is to investigate the performance of a new subcarrier allocation strategy for Orthogonal Frequency Division Multiple Access (OFDMA) multicellular networks which employ Multiple Input Multiple Output (MIMO) architecture. For this reason, a hybrid system-link level simulator has been developed executing independent Monte Carlo (MC) simulations in parallel. Up to two tiers of cells around the central cell are taken into consideration and increased loading per cell. The derived results indicate that this strategy can provide up to 12% capacity gain for 16-QAM modulation and two tiers of cells around the central cell in a symmetric2×2MIMO configuration. This gain is derived when comparing the proposed strategy to the traditional approach of allocating subcarriers that maximize only the desired user’s signal.

scholarly journals Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1844
Author(s):  
Minhoe Kim ◽  
Woongsup Lee ◽  
Dong-Ho Cho
Keyword(s):  
Communication Systems ◽  
Large Scale ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Q Learning ◽  
Multiple Input ◽  
Beam Selection ◽  
Input Multiple Output ◽  
Optimal Beam ◽  
Resource Allocation Scheme

In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes.

scholarly journals Channel Estimation Performance Analysis of FBMC/OQAM Systems with Bayesian Approach for 5G-Enabled IoT Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Han Wang ◽  
Wencai Du ◽  
Xianpeng Wang ◽  
Guicai Yu ◽  
Lingwei Xu
Keyword(s):  
Channel Estimation ◽  
Compressive Sensing ◽  
Communication Systems ◽  
Channel Model ◽  
Matching Pursuit ◽  
Multiple Input Multiple Output ◽  
Filter Bank Multicarrier ◽  
Channel Parameter ◽  
Input Multiple Output ◽  
Estimation Scheme

A filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) (FBMC/OQAM) is considered to be one of the physical layer technologies in future communication systems, and it is also a wireless transmission technology that supports the applications of Internet of Things (IoT). However, efficient channel parameter estimation is one of the difficulties in realization of highly available FBMC systems. In this paper, the Bayesian compressive sensing (BCS) channel estimation approach for FBMC/OQAM systems is investigated and the performance in a multiple-input multiple-output (MIMO) scenario is also analyzed. An iterative fast Bayesian matching pursuit algorithm is proposed for high channel estimation. Bayesian channel estimation is first presented by exploring the prior statistical information of a sparse channel model. It is indicated that the BCS channel estimation scheme can effectively estimate the channel impulse response. Then, a modified FBMP algorithm is proposed by optimizing the iterative termination conditions. The simulation results indicate that the proposed method provides better mean square error (MSE) and bit error rate (BER) performance than conventional compressive sensing methods.

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