The Impact of Statistical Noise Modeling on the Error-Rate Performance of OFDM Power-Line Communications

Abstract

In this paper, the impact of statistical noise modelling on the error performance achieved by orthogonal frequency-division multiplexing (OFDM) over indoor broadband power-line channels is investigated. Different classes of statistical models suitable to represent power-line noise are illustrated and their impact on the error performance of a specific OFDM system is assessed via computer simulations. Numerical results are compared with the error performance provided by the same system in the presence of measured power-line noise; this is evidence that a realistic indication of error performance can be achieved only if the power spectral density of the adopted noise model exhibits a good match with that of the measured noise. In practice, this result can be achieved by modeling the power-line noise as a moving average random process of proper order; however, a satisfying match can be achieved as well if other simple noise models available in the technical literature are adopted

On the Capacity of MIMO Broadband Power Line Communications Channels

“…Moreover, ifñ r =ñ t andG ĩ , 0 is non-singular for everyĩ ∈P, then C PLC also satisfies Nakagami-m model [4], and the GM 7 model [8]. We first consider the case in which the noise is an i.i.d.…”

“…Next, we consider an additive multivariate real-valued GM noise, which is another common model for BB-PLC noise, see, e.g., [8]. This model is again obtained by representing the complex- γ n = 1, the set of mean vectors {m n } n G n=1 , and the set of covariances matrices {C n } n G n=1 .…”

“…BB-PLC noise models. As in many BB-PLC systems the noise process is modeled as a temporally correlated [8]- [12] cyclostationary process [9], [13], [14], we propose an approach for extending the derivation of the differential entropy for i.i.d. noise models studied in Subsections IV-A-IV-B to correlated non-Gaussian cyclostationary noise models.…”

“…2.6]. The resulting overall BB-PLC noise is generally modeled as a non-Gaussian [4]- [9], temporally correlated [8]- [12], non-stationary [9], [13]- [16] process, and MIMO BB-PLC noise components at different output ports are typically assumed to be correlated [2], [3], [20]. The channel impulse response (CIR) in BB-PLC channels is typically modeled as a multipath channel [9], [17] with periodic variations [13], [18], [19], where the channel outputs typically contain crosstalk from other wires [2], [3], [21]- [23].…”

“…The channel impulse response (CIR) in BB-PLC channels is typically modeled as a multipath channel [9], [17] with periodic variations [13], [18], [19], where the channel outputs typically contain crosstalk from other wires [2], [3], [21]- [23]. Common models for the marginal probability density function (PDF) of BB-PLC noise include the Nakagami-m distribution [4], the Middleton class A distribution [24], and the Gaussian mixture (GM) distribution [8], [25]. All these models characterize only the marginal PDF of the additive noise process, while the complete statistics of the noise process (i.e., the joint PDF of any finite set of sample times) has not been characterized.…”