Sparse Channel Estimation of Underwater TDS-OFDM System Using Look-Ahead Backtracking Orthogonal Matching Pursuit

Junejo, Naveed Ur Rehman; Esmaiel, Hamada; Zhou, Mingzhang; Sun, Haixin; Qi, Jie; Wang, Junfeng

doi:10.1109/access.2018.2881766

Cited by 38 publications

(20 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the sparsity of the channel is not constant, which means it can be time-varying or changed by the superposition of the subchannels. The Basis Pursuit (BP) method or the SAMP can be used to solve the problem of unknown sparsity [38]. Hence, the solution of BP and an improved SAMP algorithm are proposed in this paper.…”

Section: A Compressive Estimation For Sparse Channelsmentioning

confidence: 99%

See 1 more Smart Citation

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

Zhang

Gao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In this paper, we investigate the channel estimation and decoding methods exploiting the channel sparsity in pilot-assisted Multiple-Input Multiple-Output (MIMO) Vector Orthogonal Frequency Division Multiplexing (V-OFDM) systems. Based on the sparse multipath channels, we utilize orthogonal and non-orthogonal pilot schemes to design the compressed sensing (CS) measurement process. For the optimization of the sensing matrix, we discuss the influence of pilot search algorithms and evaluation criteria and propose a particle swarm optimization (PSO) based pilot search algorithm with the simplified evaluation criterion to improve the pilot design procedure. Meanwhile, the effect of pilot insertion on the Peak-to-Average Power Ratio (PAPR) is reduced by a particular precoding matrix method without affecting the decoding complexity. Simulation data are used to evaluate the classical sparsity adaptive matching (SAMP) algorithms and the proposed Variable Threshold SAMP (VTSAMP) algorithm, and the results show that the improved method has higher channel estimation accuracy with unknown sparsity. On the other hand, to overcome the complexity of CS-based decoding, we design the fully connected Deep Neural Network (FC-DNN) decoders, which combine the results of channel estimation results with the prevalent neural network technology. We observe that when the sparse channels are estimated accurately by CS methods, the proposed FC-DNN can achieve the same performance as the high-precision linear decoder by using the time-domain pilots and channel estimation results. INDEX TERMS MIMO, OFDM, compressed sensing, channel estimation, peak to average power ratio, decoding, neural networks.

show abstract

Section: A Compressive Estimation For Sparse Channelsmentioning

confidence: 99%

“…The estimated value of superposed sparse channels,ĥ. We can transform solving (38), which is a BP problem, into finding another form called the Basis Pursuit De Noising (BPDN). The form is min h <t,r> 1 2…”

Section: Outputmentioning

confidence: 99%

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

Zhang

Gao

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In [13], based on pseudo-noise code, a frame synchronization method with low cost and low resource consumption has been proposed. Also, pseudo-noise sequence can be adopted as a guard interval or training sequence for channel estimation and synchronization which is known to transmitter and receiver [14]. In [15], a time synchronization method based on TD-LTE frame synchronization was studied.…”

Section: A Literature Reviewmentioning

confidence: 99%

A Spectrum Efficient Constellation to Simultaneously Transmit Information and Synchronization Sequence

Yao

Yuan

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In this paper, a new type of quadrature phase shift keying (QPSK) modulation, named as special QPSK (SQPSK), is proposed and optimized. For SQPSK, the quadrant of the symbol is determined by the bits in information sequence according to QPSK constellation, while the offset relative to the original QPSK constellation is controlled by the bits in synchronization sequence. Under this scheme, the simultaneous transmission of information and synchronization sequences is implemented to improve spectrum efficiency with low computational complexity. In order to achieve the best balance beteween the bit error rate (BER) and the synchronization performance, we optimize the parameter design. Furthermore, a joint synchronization scheme is proposed for frame synchronization, frequency offset estimation, and frequency offset compensation to solve the performance loss caused by Doppler frequency shift in practical applications. Then based on the characteristics of the constellation map, a dynamic threshold demodulation scheme which further improves the BER performance is discussed. Finally, some simulation results are given to show that the SQPSK not only has both good error performance and acquisition performance, but also improves the spectrum utilization of channel resource significantly. INDEX TERMS Bit error rate (BER), Doppler shift, frame synchronization, quadrature phase shift keying (QPSK).

show abstract

“…Underwater acoustic communication (UWAC) has become one of the critical research fields due to its proliferation of new scientific, industrial, and naval applications [1][2][3]. However, the limitation of the available bandwidth, poor physical link quality, availability of a sustainable energy source for the battery feed sensor nodes, high latency, and attenuation of the acoustic waves by water still represent the main drawbacks of the wireless communication in the ocean's environment [3][4][5]. Although optical communication, electromagnetic communication, and magnetic induction communication can be employed for the oceanic environment, acoustic communication is considered to be the widest in use since it achieves long communication distances of tens of kilometers for the low data transmission rate and several tens of meters for the high data transmission rate [6,7].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication

Qasem

Esmaiel

Sun

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

The limitation of the available channel bandwidth and availability of a sustainable energy source for battery feed sensor nodes are the main challenges in the underwater acoustic communication. Unlike terrestrial’s communication, using multi-input multi-output (MIMO) technologies to overcome the bandwidth limitation problem is highly restricted in underwater acoustic communication by high inter-channel interference (ICI) and the channel multipath effect. Recently, the spatial modulation techniques (SMTs) have been presented as an alternative solution to overcome these issues by transmitting more data bits using the spatial index of antennas transmission. This paper proposes a new scheme of SMT called spread-spectrum fully generalized spatial modulation (SS-FGSM) carrying the information bits not only using the constellated data symbols and index of active antennas as in conventional SMTs, but also transmitting the information bits by using the index of predefined spreading codes. Consequently, most of the information bits are transmitted in the index of the transmitter antenna, and the index of spreading codes. In the proposed scheme, only a few information bits are transmitted physically. By this way, consumed power transmission can be reduced, and we can save the energy of underwater nodes, as well as enhancing the channel utilization. To relax the receiver computational complexity, a low complexity deep learning (DL) detector is proposed for the SS-FGSM scheme as the first attempt in the underwater SMTs-based communication. The simulation results show that the proposed deep learning detector-based SS-FGSM (DLSS-FGSM), compared to the conventional SMTs, can significantly improve the system data rate, average bit error rate, energy efficiency, and receiver’s computational complexity.

show abstract

Sparse Channel Estimation of Underwater TDS-OFDM System Using Look-Ahead Backtracking Orthogonal Matching Pursuit

Cited by 38 publications

References 37 publications

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

Pilot-Assisted MIMO-V-OFDM Systems: Compressed Sensing and Deep Learning Approaches

A Spectrum Efficient Constellation to Simultaneously Transmit Information and Synchronization Sequence

Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication

Contact Info

Product

Resources

About