Abstract
The requirement of high spectrum efficiency puts forward higher requirements on frame synchronization (FS) in wireless communication systems. Meanwhile, a large number of nonlinear devices or blocks will inevitably cause nonlinear distortion. To avoid the occupation of bandwidth resources and overcome the difficulty of nonlinear distortion, an extreme learning machine (ELM)-based network is introduced into the superimposed training-based FS with nonlinear distortion. Firstly, a preprocessing procedure is utilized to reap the features of synchronization metric (SM). Then, based on the rough features of SM, an ELM network is constructed to estimate the offset of frame boundary. The analysis and experiment results show that, compared with existing methods, the proposed method can improve the error probability of FS and bit error rate (BER) of symbol detection (SD). In addition, this improvement has its robustness against the impacts of parameter variations.
Highlights
Due to the limited bandwidth resources, wireless communication systems have pursued high spectrum efficiency in the past few decades [1]
These promising frame synchronization (FS) promote us to develop further explorations, especially for the scenarios with nonlinear distortion. Inspired by those advantages of extreme learning machine (ELM) networks and superimposed training, we investigate an ELM-based FS by using superimposed training, which overcomes the challenges from spectrum efficiency and nonlinear distortion during the FS phase
RELATED WORKS We respectively present the related works of deep learning (DL)-based FS and ELM-based FS as follows
Summary
Due to the limited bandwidth resources, wireless communication systems have pursued high spectrum efficiency in the past few decades [1]. The superimposed training-based FS has been investigated in past years, e.g., [22]–[25] These promising FSs promote us to develop further explorations, especially for the scenarios with nonlinear distortion. The merits to cope with nonlinear distortion can be reaped by ELM networks, and high spectrum efficiency can be achieved by using superimposed training. B. CONTRIBUTIONS To overcome the challenges of spectrum efficiency and nonlinear distortion during the FS phase, the ELM-based FS using superimposed training is investigated in this paper. In contrast to the ELM-based time-division FS scheme in [9], the occupation of bandwidth resources is avoided in the proposed FS method, and the smaller error probability of FS is achieved with the same energy cost. Notations: Bold face upper case and lower case letters denote matrix and vector respectively. (·)T , (·)H , (·)†, denote the transpose, conjugate transpose, and matrix pseudoinverse, respectively. · 2 is the Frobenius norm. |x| denotes the absolute value of x and |x| denotes the absolute value operation to the entry-wise of vector x
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