Design and Detection of Multilinear Chirp Signals for Underwater Acoustic Sensor Networks

Abstract

In Underwater Acoustic Sensor Networks (UW-ASNs), some key problems have attracted more and more attention, including power consumption, performance of multiple access, and complexity of node. Motivated by finding reduced power consumption and improved performance of multiple access in UW-ASNs, a multilinear chirp-Code Division Multiple Access (MLC-CDMA) scheme is proposed. The differences between single slope chirp signal and multilinear chirp signal are analyzed in the paper. At the receiving end, a new detection technique called mixing-change rate along with fractional Fourier transform (MCR-FrFT) is proposed to detect the multirate chirp (MRC) signal and reduce complexity of node. There are two steps to realize the detection technique MCR-FrFT. By using the MCR-FrFT, the computation of detection can be decreased to 50% compared with direct FrFT. The simulation results indicate that, using the MCR-FrFT technique, the different users' signal can be separated and detected rapidly.

Inter-Relay Interference Mitigation for Chirp-Based Two-Path Successive Relaying Protocol

“…In order to successively transmit and receive the data in the TPSR protocol, we consider the MLC-based BOK-CSS scheme in which the source and two relays transmit the data with a different separating bandwidth. The MLC for the k -th relay according to the bit i is [27] cfi,k()t1=A·prefixcos()2πf0t1+πμfi,kt12, cbi,k()t2=A·prefixcos()2πf0+Bfi,kt2−Tc2+πμbi,k()t2−Tc22, where A=Es/Tc, Es is the chirp energy, and 0⩽t1⩽Tc/2 and Tc/2⩽t2⩽…”

A fractional Fourier transform–based channel estimation algorithm in single-carrier direct sequence code division multiple access underwater acoustic communication system

“…Set T 1 as the time interval between the two LFM signals, assuming that the received combined LFM signal has no frequency shift, and the FRFT of signal is performed. When the rotation angle is a, it can form a series of peaks in the corresponding u-domain; according to the definition of FRFT 22 and equation (1), the peak interval in the corresponding u-domain of the two LFM signals can be obtained from equation (6) Assuming K e is the chirp rate of the received LFM signal, T is the time duration of the combined LFM signal in the transmitter; in equation 7, the relationship between K and K e can be expressed as 24…”

“…Let Du i be the u-domain interval of the ith path of the two LFM signals and e i is the central frequency shift of the ith path for the LFM1 signal. After performing the FRFT for the received combined LFM signal, the corresponding u-domain interval for each pair of peaks can be obtained according to its u-domain figure and equation (6), which can be shown in equation 8Du…”

“…To improve the performance of underwater acoustic communication for sensor network, [1][2][3] the estimation of underwater acoustic channel parameters in real time is needed, which mainly includes the transmitted signal design and its detection algorithm. [4][5][6] In general, the time delay and Doppler frequency shift joint estimation can be achieved by means of resampling and timedomain correlation, 7 which can be only applied to same Doppler frequency shift for multi-path. With the development of underwater acoustic sensor networks, multi-user simultaneous communication is also essential 8,9 and some multiple-access technologies are also proposed for distinguishing multi-user.…”

Machine Learning Algorithms for Identification of Low Signal-to-Noise Ratio Radar Signals

“…It was noted that a windowed chirp rate approach only detected small chirp rates at low SNR and that discrete ambiguity needs high SNR. Cheng et al [23] used the MCR-FrFT to detect multilinear chirp signals for underwater acoustic sensor networks.…”