MIMO电力线通信信道中马尔可夫-米德尔顿和米德尔顿A类噪声模型误码率性能比较

B. Rajkumarsingh, Muhammad Husayn Edoo
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摘要

近几十年来,室内电力线通信(PLC)已经引起了人们的极大兴趣。除了宽带数据服务和不需要新的基础设施等优势外,每栋建筑物中电力线的可用性和拓扑结构增加了这项技术的吸引力。然而,由于信号衰减、多径传播、干扰和噪声,特别是脉冲噪声,电力线是一个恶劣的数据传输环境。在这项工作中,对室内宽带PLC信道进行建模,该信道使用多输入多输出(MIMO)技术以及OSTBC,目的是提高误码率性能(BER)。对于脉冲噪声的突发行为,考虑了参数来源于米德尔顿A类噪声模型的马尔可夫-米德尔顿噪声模型。这两种脉冲噪声模型都在MIMO PLC信道中实现。已经发现,MIMO与正交空时分组码(OSTBC)在减少多径传播和频率选择性衰落方面非常有效,因为与单输入单输出(SISO)相比,在BER为10^{-5}$时观察到14.93 dB的增益。随着脉冲量的增加,系统间的信噪比差从0.99 dB增加到4.04 dB,马尔可夫-米德尔顿噪声对系统的降噪作用略大于米德尔顿A类噪声。
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BER Performance Comparison of the Markov-Middleton and Middleton Class A Noise Models in the MIMO Power Line Communication Channel
Power Line Communication (PLC) for indoor use has been of significant interest in recent decades. The availability and topology of power lines in every building besides advantages such as broadband data services and no need for new infrastructure, increase the appeal of this technology. However, power lines are a harsh environment for the transmission of data due to signal attenuation, multipath propagation, interference and noise, especially impulsive noise. In this work, an indoor broadband PLC channel is modelled which uses Multiple Input Multiple Output (MIMO) techniques along with OSTBC with an objective to improve the bit error rate performance (BER). For the bursty behaviour of impulsive noise, the Markov-Middleton noise model which has its parameters derived from the Middleton Class A noise model is considered. Both impulsive noise models are implemented in the MIMO PLC channel. It has been found that MIMO along with Orthogonal Space Time Block Codes (OSTBC) is greatly effective in reducing multipath propagation and frequency-selective fading as a gain of14.93 dB at a BER of $10^{-5}$ is observed when compared to the Single Input Single Output (SISO) counterpart. It was also found that Markov-Middleton noise degrades the system slightly more than Middleton Class A noise as the SNR difference between the systems increases from 0.99 dB to 4.04 dB when the impulsiveness increases.
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