Transmitter Identification Signal Detection Algorithm for ATSC 3.0 Single Frequency Networks

2020 ◽  
Vol 66 (3) ◽  
pp. 737-743
Author(s):  
Jaekwon Lee ◽  
Sunhyoung Kwon ◽  
Sung-Ik Park ◽  
Dong Ku Kim
1981 ◽  
Vol 71 (4) ◽  
pp. 1351-1360
Author(s):  
Tom Goforth ◽  
Eugene Herrin

abstract An automatic seismic signal detection algorithm based on the Walsh transform has been developed for short-period data sampled at 20 samples/sec. Since the amplitude of Walsh function is either +1 or −1, the Walsh transform can be accomplished in a computer with a series of shifts and fixed-point additions. The savings in computation time makes it possible to compute the Walsh transform and to perform prewhitening and band-pass filtering in the Walsh domain with a microcomputer for use in real-time signal detection. The algorithm was initially programmed in FORTRAN on a Raytheon Data Systems 500 minicomputer. Tests utilizing seismic data recorded in Dallas, Albuquerque, and Norway indicate that the algorithm has a detection capability comparable to a human analyst. Programming of the detection algorithm in machine language on a Z80 microprocessor-based computer has been accomplished; run time on the microcomputer is approximately 110 real time. The detection capability of the Z80 version of the algorithm is not degraded relative to the FORTRAN version.


2013 ◽  
Vol 756-759 ◽  
pp. 3183-3188
Author(s):  
Tao Lei ◽  
Deng Ping He ◽  
Fang Tang Chen

BLAST can achieve high speed data communication. Its signal detection directly affects performance of BLAST receiver. This paper introduced several signal detection algorithmsZF algorithm, MMSE algorithm, ZF-SIC algorithm and MMSE-SIC algorithm. The simulation results show that the traditional ZF algorithm has the worst performance, the traditional MMSE algorithm and the ZF-SIC algorithm is similar, but with the increase of the SNR, the performance of ZF-SIC algorithm is better than MMSE algorithm. MMSE-SIC algorithm has the best detection performance in these detection algorithms.


2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Gaoli Zhao ◽  
Jianping Wang ◽  
Wei Chen ◽  
Junping Song

The MIMO-OFDM system fully exploits the advantages of MIMO and OFDM, effectively resisting the channel multipath fading and inter-symbol interference while increasing the data transmission rate. Studies show that it is the principal technical mean for building underwater acoustic networks (UANs) of high performance. As the core, a signal detection algorithm determines the performance and complexity of the MIMO-OFDM system. However, low computational complexity and high performance cannot be achieved simultaneously, especially for UANs with a narrow bandwidth and limited data rate. This paper presents a novel signal detection algorithm based on generalized MMSE. First, we propose a model for the underwater MIMO-OFDM system. Second, we design a signal coding method based on STBC (space-time block coding). Third, we realize the detection algorithm namely GMMSE (generalized minimum mean square error). Finally, we perform a comparison of the algorithm with ZF (Zero Forcing), MMSE (minimum mean square error), and ML (Maximum Likelihood) in terms of the BER (bit error rate) and the CC (computational complexity). The simulation results show that the BER of GMMSE is the lowest one and the CC close to that of ZF, which achieves a tradeoff between the complexity and performance. This work provides essential theoretical and technical support for implementing UANs of high performance.


2018 ◽  
Vol 189 ◽  
pp. 04006
Author(s):  
Nan Wang ◽  
Yunshan Xu ◽  
Haibao Xia ◽  
Jundi Wang

In this paper, a fusion detection algorithm that focuses on decentralized CFAR (Constant False Alarm Rate) signal detection problem without prior information is proposed. In the algorithm, the threshold and test statistic of the detection fusion algorithm derive from the conventional CFAR detection method. At last a framework for decentralized CFAR signal detection is designed corresponding to the fusion algorithm. Simulation results illustrate that an almost optimal detection performance is obtained by the proposed algorithm.


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