A novel signal detection algorithm of multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time for underwater acoustic networks based on the improved minimum mean square error

Gaoli Zhao,Wei Chen,Jianping Wang,Junping Song

doi:10.1177/1550147720979898

Abstract

Multiple-input multiple-output is a commonly used technology supporting for high-rate transmission over frequency-selective fading channels with multiple antennas. Vertical-Bell Laboratories Layered Space-Time is a detection method of a multiple-input multiple-output system, which establishes a direct correspondence between antennas and layers. Studies demonstrate that multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time is a meaningful way for underwater acoustic networks of high performance. However, considering the hardware constraints and energy consumption, achieving a trade-off between the bit error ratio and complexity is a crucial issue for underwater acoustic networks of multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time systems. This article proposes a novel signal detection algorithm of multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time. First, we address the unitary matrix of the underwater acoustic channel by LDLHdecomposition. Second, we order the detection sequence based on the permutation matrix. Third, we detail the implementation of interference cancelation and slice processing. Finally, we perform experiments for comparing the bit error ratio, energy consumption, processing delay, and complexity of the proposed algorithm with zero-forcing Vertical-Bell Laboratories Layered Space-Time, minimum mean square error Vertical-Bell Laboratories Layered Space-Time, and maximum likelihood Vertical-Bell Laboratories Layered Space-Time. Results indicate that our algorithm maintains bit error ratio and the processing delay to that of maximum likelihood Vertical-Bell Laboratories Layered Space-Time algorithm. However, it reduces the energy consumption, which achieves a good trade-off between performance and complexity. This work supports on constructing underwater acoustic networks of multiple-input multiple-output Vertical-Bell Laboratories Layered Space-Time system.

Highlights

It is a solution to realize the high-performance communication based on layered space–time codes (STCs), which has a good potential for improving the spectrum utilization.[7]
The main contributions of this article list as follows: first, we design a method for resolving the unitary matrix Q of the underwater acoustic (UWA) channel based on LDLH decomposition; second, we use the permutation matrix to sort the detection sequence; third, we implement the interference elimination and slice processing; and we perform experiments for comparing the bit error ratio (BER), energy consumption, processing delay, and complexity of the proposed algorithm with ZF Vertical-Bell Laboratories Layered SpaceTime (V-BLAST), minimum mean square error (MMSE) V-BLAST, and maximum likelihood (ML) V-BLAST
Our algorithm is based on improved MMSE, the complexity is reflected in executing the LDLH decomposition for solving the channel unitary matrix Q, and determining the detection sequence based on the matrix permutation

Summary

Introduction

Underwater acoustic networks (UANs) have a wide range of application scenarios, such as marine data. Nodes are powered by batteries, and it will shorten the survival time of UANs. in MIMO V-BLAST systems, it is a hot topic for designing a detection algorithm based on the trade-off between performance and complexity. The main contributions of this article list as follows: first, we design a method for resolving the unitary matrix Q of the UWA channel based on LDLH decomposition; second, we use the permutation matrix to sort the detection sequence; third, we implement the interference elimination and slice processing; and we perform experiments for comparing the BER, energy consumption, processing delay, and complexity of the proposed algorithm with ZF V-BLAST, MMSE V-BLAST, and ML V-BLAST. The simulation is discussed in section ‘‘Simulation and results.’’ section ‘‘Conclusion’’ concludes this article

Related work

Findings

Conclusion