Channel estimation for sparse channel OFDM systems using least square and minimum mean square error techniques

Farzamnia, Ali; Hlaing, Ngu War; Haldar, Manas K.; Rahebi, Javad

doi:10.1109/icengtechnol.2017.8308193

Cited by 19 publications

(10 citation statements)

References 10 publications

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“…In [99], two efficient narrowband demodulation reference signal (NDMRS)assisted channel estimation algorithms have been proposed. The NDMRS algorithms are based on the conventional least-squared (LS) [100], and minimum means square error (MMSE) [101] methods. The results of the theoretical analysis and link-level performance illustrate that the proposed algorithms perform much better than the traditional LS and MMSE methods in low SNR conditions.…”

Section: Channel Estimation and Error Correctionmentioning

confidence: 99%

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

Hancke

Abu‐Mahfouz

2020

IEEE Access

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Narrowband Internet of Things (NB-IoT) is a type of low-power wide-area (LPWA) technology standardized by the 3 rd-Generation Partnership Project (3GPP) and based on long-term evolution (LTE) functionalities. NB-IoT has attracted significant interest from the research community due to its support for massive machine-type communication (mMTC) and various IoT use cases that have stringent specifications in terms of connectivity, energy efficiency, reachability, reliability, and latency. However, as the capacity requirements for different IoT use cases continue to grow, the various functionalities of the LTE evolved packet core (EPC) system may become overladen and inevitably suboptimal. Several research efforts are ongoing to meet these challenges; consequently, we present an overview of these efforts, mainly focusing on the Open System Interconnection (OSI) layer of the NB-IoT framework. We present an optimized architecture of the LTE EPC functionalities, as well as further discussion about the 3GPP NB-IoT standardization and its releases. Furthermore, the possible 5G architectural design for NB-IoT integration, the enabling technologies required for 5G NB-IoT, the 5G NR coexistence with NB-IoT, and the potential architectural deployment schemes of NB-IoT with cellular networks are introduced. In this article, a description of cloud-assisted relay with backscatter communication, a comprehensive review of the technical performance properties and channel communication characteristics from the perspective of the physical (PHY) and medium-access control (MAC) layer of NB-IoT, with a focus on 5G, are presented. The different limitations associated with simulating these systems are also discussed. The enabling market for NB-IoT, the benefits for a few use cases, and possible critical challenges related to their deployment are also included. Finally, present challenges and open research directions on the PHY and MAC properties, as well as the strengths, weaknesses, opportunities, and threats (SWOT) analysis of NB-IoT, are presented to foster the prospective research activities.

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Section: Channel Estimation and Error Correctionmentioning

confidence: 99%

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

Hancke

Abu‐Mahfouz

2020

IEEE Access

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“…With the assistance of channel estimation techniques, the noise within the wireless channel is estimated that get additional within the transmitted information and channel equalization technique removes the error, that's estimated from the channel, from the received information and provides the corrected received output to the receiver [8].…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Computer simulations show that the proposed scheme can acquire the information of channels accurately and has the ability to resist nonlinear distortion and fading without pre-training and feedback link between receiver and transmitter. Similarly, in [8] proposed Least square (LS) method is considered for sparse channels. Channel coefficients for OFDM are estimated by applying this method and Minimum Mean Square Error (MMSE) method.…”

Section: ) Comparison With Existing Workmentioning

confidence: 99%

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SoftMax Recurrent Neural Network based Faded Channel Estimation using FIM in MIMO-OFDM

Ansari

Bhatt

2020

Smart Moves Journal Ijoscience

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The combination of MIMO technology with OFDM system, there is enhancement of wireless digital communication which is quite beneficial for future communicating system. MIMO-OFDM improves the efficiency and quality of the wireless scenario. With efficient channel estimation technique especially non-blind under MIMO-OFDM scenario present an enhanced performance with low complexity. In the pilot type, the least squares method (LS method) is less complex and requires an implicit knowledge of the channels. However, it suffers from inter-carrier interference (ICI). For this reason, the optimal design of Channel Estimator is an area of ongoing research. In this work, the performance of the DWT-OFDM scheme in combination with a multi-input system and multiple outputs for unknown pilot symbols is evaluated using a neural network. Take advantage of time series prediction using a recurrent neural network (RNN) with a SoftMax layer with frequency index modulation to perform channel prediction. In this research work a comparative analysis is performed among proposed softmax recurrent neural network based channel estimation with existing channel estimation technique for variable signal to noise ratio (Eb/No) for frequency indexed modulation techniques. The existing channel estimation techniques for MIMO-OFDM communicating environment are based on known pilots over the noisy fading wireless environment. From simulation result, it is observed and concluded that the existing channel estimation techniques gives higher BER as compared to proposed softmax recurrent neural network channel estimation technique.

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“…Bhimsing menjelaskan filter Wiener bisa menghasilkan estimasi lebih baik di kanal fading Rayleigh daripada Rician [7]. A. Farzamnia menggunakan metode Least Square (LS) dan Minimum Mean Square Error (MMSE) untuk mendapatkan estimasi kanal, dan menunjukkan bahwa metode MMSE lebih baik daripada LS [8]. Pada jurnal ini, peneliti menggunakan metode Piecewise Linear dengan 2 slope sebagai metode alternatif untuk melakukan estimasi kanal.…”

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Estimasi Kanal Sistem OFDM pada Kanal Fading Rayleigh dengan Metode Piece-wise Linear

Hadiansyah¹

2021

COMPLETE

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Transmisi data kecepatan tinggi menjadi sorotan masyarakat saat ini karena semakin banyak masyarakat menggunakan teknologi nirkabel pita lebar untuk memenuhi kebutuhan sehari-hari. Long Term Evolution (LTE) masih menjadi teknologi eksisting yang memanfaatkan sistem Orthogonal Frequency Division Multiplexing (OFDM) sebagai solusi penghematan bandwidth. Untuk mempertahankan kualitas data dari distorsi, maka dibutuhkan teknik estimasi kanal. Dengan mengetahui hasil estimasi kanal, maka akan didapatkan solusi untuk meminimalisasi efek distorsi. Respon impuls diasumsikan berupa kanal fading Rayleigh. Estimasi kanal yang dipakai adalah metode estimasi Piece-wise Linear dengan 2 slope. Serangkaian penelitian yang bisa dilakukan setelah estimasi kanal adalah estimasi efek doppler, minimalisasi interferensi antar-simbol, dan efisiensi serta optimalisasi sistem. Dari hasil simulasi menggunakan program Matlab dengan 100 kali iterasi, dapat diambil kesimpulan bahwa semakin besar Signal to Noise Ratio (SNR), semakin kecil error yang terjadi. Nilai rata-rata error estimasi dari SNR=9-40 dB adalah 2.4%-38.2%.

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Channel estimation for sparse channel OFDM systems using least square and minimum mean square error techniques

Cited by 19 publications

References 10 publications

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

Building Upon NB-IoT Networks: A Roadmap Towards 5G New Radio Networks

SoftMax Recurrent Neural Network based Faded Channel Estimation using FIM in MIMO-OFDM

Estimasi Kanal Sistem OFDM pada Kanal Fading Rayleigh dengan Metode Piece-wise Linear

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