Pilot Allocation for Distributed-Compressed-Sensing-Based Sparse Channel Estimation in MIMO-OFDM Systems

He, Xueyun; Song, Rongfang; Zhu, Wei-Ping

doi:10.1109/tvt.2015.2441743

Cited by 79 publications

(40 citation statements)

References 33 publications

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“…For compare purpose, we consider the typical 8 × 8 and 16 × 16 massive MIMO-OFDM systems. It is noteworthy that random pilot insertion is exploited in the simulation as it can enhance restrain isometric property (RIP) of the measurement matrix [35]. The specific parameters of our system in the simulations are listed in Table 1.…”

Section: Resultsmentioning

confidence: 99%

Sparse massive MIMO-OFDM channel estimation based on compressed sensing over frequency offset environment

et al. 2019

EURASIP J. Adv. Signal Process.

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In massive MIMO-OFDM systems, channel estimation is a significant module which can be utilized to eliminate multipath interference. However, in realistic communication systems, carrier frequency offset (CFO), which often exists in receive end, will deteriorate the performance of channel estimation. One of the effective solutions is to compensate CFO via the help of pseudo-noise (PN) sequence. At the beginning of this paper, to reduce system complexity and correctly compensate CFO, we propose an improved OFDM frame structure. Subsequently, we theoretically analyze the catastrophic influence of CFO on conventional PN-sequence-based compressed sensing (CS) channel estimation scheme. As our solution, based on the improved OFDM frame structure, a novel massive MIMO-OFDM channel estimation method under CFO environment is proposed. It first estimates CFO by utilizing differential correlation algorithm. Thereby, the interference caused by CFO can be eliminated. Then, relying on the PN sequence, the partial common support (PCS) information of each channel is obtained. Finally, using the PCS information as a priori information, we improve the CS reconstruction scheme to estimate the accurate channel. The simulation result shows that the proposed scheme demonstrates better MSE and BER performance than other mentioned schemes. The major advantage of our scheme is its anti-CFO ability and independence to channel sparsity level. Therefore, the proposed scheme is meaningful for practical use.

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Section: Resultsmentioning

confidence: 99%

Sparse massive MIMO-OFDM channel estimation based on compressed sensing over frequency offset environment

et al. 2019

EURASIP J. Adv. Signal Process.

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“…This MIMO system model formulation allows the study of filter bank multi-carrier communication, partial FFT demodulation and waveform diversity design [20][21][22][23]. While in the channel sounding mode, a series of predefined pulses z i , i ∈ Z M is transmitted [24]. These pulses are compared with the received signals w j , j ∈ Z N for characterizing the MIMO channel using the inverse relation shown in Equation (6).…”

Section: Delay-doppler Mimo Channel Characterizationmentioning

confidence: 99%

A Framework for Multiple Object Tracking in Underwater Acoustic MIMO Communication Channels

Rodriguez

Aceros

Valera

et al. 2017

JSAN

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This work presents a computational framework for the analysis and design of large-scale algorithms utilized in the estimation of acoustic, doubly-dispersive, randomly time-variant, underwater communication channels. Channel estimation results are used, in turn, in the proposed framework for the development of efficient high performance algorithms, based on fast Fourier transformations, for the search, detection, estimation and tracking (SDET) of underwater moving objects through acoustic wavefront signal analysis techniques associated with real-time electronic surveillance and acoustic monitoring (eSAM) operations. Particular importance is given in this work to the estimation of the range and speed of deep underwater moving objects modeled as point targets. The work demonstrates how to use Kronecker products signal algebra (KSA), a branch of finite-dimensional tensor signal algebra, as a mathematical language for the formulation of novel variants of parallel orthogonal matching pursuit (POMP) algorithms, as well as a programming aid for mapping these algorithms to large-scale computational structures, using a modified Kuck's paradigm for parallel computation.

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“…Specifically, the authors in [17] proposed a genetic algorithm (GA)-based pilot allocation algorithm in CS-based channel estimation. Furthermore, in [18] a suboptimal pilot allocation algorithm is designed based on GA for DCS-based channel estimation. Moreover, Qi et al in [19] utilize the estimation of distribution algorithm (EDA) to define optimized pilot positions in SISO-OFDM systems.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, Qi et al in [19] utilize the estimation of distribution algorithm (EDA) to define optimized pilot positions in SISO-OFDM systems. Moreover, He et al [18] and Akbarpour-Kasgari and Ardebilipour [13] generate optimized pilot sequences for MIMO-OFDM compressed channel estimation using a GA and cross entropy (CE)-based approach.…”

Section: Introductionmentioning

confidence: 99%

Improving MIMO relay compressed sensing-based channel estimation and pilot allocation

Akbarpour-Kasgari

Ardebilipour

2019

J Wireless Com Network

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Increasing the data rate of communication systems together with the performance are main goals of the communication networks in the future. Multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) relay networks as a key technology is one the main techniques to maintain the performance and data rate. Being influenced by fading channels, the MIMO-OFDM relay networks need some reliable approaches to estimate the channel response. Compressed sensing (CS) is one of the critical approaches to maintain the accuracy together with the spectral efficiency. In this paper, we have utilized CS-based approaches to estimate the MIMO-OFDM relay channel. Specifically, forward backward prediction (FBP) is used to estimate the fading channels. This approach benefits from backward correction which distinguishes it from other iterative approaches and helps interestingly in channel estimation accuracy. Moreover, in order to improve the channel estimation, pilot allocation approaches are proposed based on the system model and probability function. Furthermore, a cross entropy-based approach is utilized to propose two different approaches called sequential cross entropy self-coherence (SCE-SC) and parallel cross entropy self-coherence. Actually, mutual coherence is divided into two parts namely cross coherence and self coherence. It is demonstrated that in FBP-based approach self-coherence is important in mutual coherence. Consequently, the computations are interestingly decreased. The superiority of the method is represented using comparing simulations with other well-known approaches.

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Pilot Allocation for Distributed-Compressed-Sensing-Based Sparse Channel Estimation in MIMO-OFDM Systems

Cited by 79 publications

References 33 publications

Sparse massive MIMO-OFDM channel estimation based on compressed sensing over frequency offset environment

Sparse massive MIMO-OFDM channel estimation based on compressed sensing over frequency offset environment

A Framework for Multiple Object Tracking in Underwater Acoustic MIMO Communication Channels

Improving MIMO relay compressed sensing-based channel estimation and pilot allocation

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