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Journal of Electrical and Computer Engineering
Volume 2015, Article ID 706526, 7 pages
Research Article

Performance Analysis of Precoded MIMO PLC System Based on Two-Sided Jacobi SVD

1SERCOM Laboratory, Tunisia Polytechnic School, Carthage University, 2078 La Marsa, Tunisia
2CodinTek Company, El Ghazala Technological Park, 2088 Ariana, Tunisia

Received 5 August 2015; Accepted 19 November 2015

Academic Editor: Adam Panagos

Copyright © 2015 Hasna Kilani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper evaluates the performance of closed loop multiple input multiple output power line communication (CL MIMO PLC) system based on enhanced zero-forcing (ZF) equalizer. In this work, the two-sided Jacobi (TSJ) algorithm has been investigated for the computation of singular value decomposition of the channel matrix. Quantized parameters are feedback from the receiver to the transmitter for precoding process. Numerous simplifications are introduced for the reduction of the algorithm complexity. The performance of the CL MIMO PLC is evaluated in terms of bit error rate (BER), constellation error vector magnitude (EVM), and mean square error (MSE) between the constructed SVD matrices and Matlab computed ones.

1. Introduction

The PLC systems present the new trend for high level communication. The application of MIMO scenarios on PLC systems enhances the data throughput significantly. In MIMO wireline systems [1], the data streams can be demultiplexed into several substreams transmitted by different ports to improve the throughput performance of the overall communication system by utilizing the transmit diversity [2, 3].

Various architectures of receivers has been proposed in literature such as the zero-forcing (ZF) receiver, the minimum mean square error (MMSE) receiver, and the successive interference canceller (SIC) receiver. These techniques are investigated to decode the spatially multiplexed signals over MIMO systems [46]. Generally, the performance improvement from one type of the MIMO receiver to another comes at the price of higher implementation cost. For example, despite its reduced complexity of implementation, the ZF receiver is known to suffer from the effect of noise enhancement.

One can enhance the performance of the ZF receiver, by splitting the equalization algorithm among the transmitter and the receiver. The TSJ-SVD is used for the computing of precoding and decoding matrix. Several simplifications are introduced to reduce the hardware implementation of the precoding/decoding processes.

The remainder of the paper is organized as follows. In Section 2, the CL MIMO system is described. The precoding design scheme is then presented in Section 3 and introduced simplifications making computing complexity lower. Subsequently, the performance analysis of TSJ-SVD algorithm is given in Section 4. Finally, the paper is concluded in Section 5.

The superscript denotes the conjugate transpose. In addition, and represent the pseudoinverse and transpose operations, respectively. denotes the set of matrices over complex field.

2. System Description

For a MIMO PLC system composed of transmission ports and reception ports, the MIMO channel can be described by a complex matrix . The MIMO PLC model is then given by where is the transmit signal, is the received signal vector, and is the noise at the receiver. In the remainder of the paper and . The channel matrix has the following formula: where is the complex coefficient, and .

In conventional MIMO communication system based on zero-forcing (ZF) equalizer, the equalization matrix is given by

The main idea of the closed loop MIMO system is based on decomposing the channel matrix into precoding and decoding parts. The precoding matrix is then sent by the receiver to the transmitter in a feedback in order to precode the signal before being sent.

Here the decomposition is carried out by the SVD method largely used in MIMO. The channel matrix can be decomposed into parallel and independent SISO branches by SVD (see Figure 1):where is the right-hand unitary matrix of the SVD, is the left-hand unitary matrix, and is a diagonal matrix containing the singular values of the channel matrix .

Figure 1: SVD based MIMO transmitter and receiver.

In order to improve the MIMO equalization, rather than using simple ZF equalizer (2), the precoding matrix is incorporated in (1) by replacing the transmit symbol vector by

Equation (1) becomes

If we denote as the reception first-stage matrix, the received signal crossing is

If then

The combination of precoding, channel, and reception first-stage matrices decomposes the equivalent channel into parallel streams reduced to the diagonal matrix (see Figure 1). Then the ZF equalizer matrix becomes

The precoding matrix mixes the symbols of , and thus the transmit symbol vector contains all symbols of ; that is, each symbol is transmitted via each MIMO path. Thus, the full spatial diversity is achieved. The receiver side model is depicted in Figure 2. In this work, the MIMO system is considered.

Figure 2: Receiver side of precoded MIMO PLC system.

3. The Precoding Process

The proposed MIMO scheme is mainly based on the precoding process. After estimating the channel matrix , its SVD is carried out using the TSJ method.

In this section, we introduce the TSJ-SVD algorithm. Then, we describe the main steps of the proposed simplified algorithm.

3.1. Basic Transformation on the Channel Matrix

In the literature, the TSJ algorithm is only used for real symmetric matrix [7]. In our case, the channel matrix is complex. So to apply the TSJ algorithm we should transform the complex matrix into real symmetric matrix. We then firstly proceed by transforming the matrix into Hermitian matrix :where , , and are real values, is an angle, and is a Hermitian matrix such that(i);(ii)The diagonal elements of are real; only the off-diagonals are complex conjugates:where .

The obtained matrix is real and symmetric and the Jacobi algorithm can be applied to decompose it into SVD form.

3.2. The Two-Sided Jacobi Transformation

As the MIMO system needs to feed back the precoding matrix to the transmitter, an optimization of the transferred parameters is fundamental to make the transferred parameters transfer overhead as little as possible. The transformation of to is carried out for this aim as will be detailed below.

The TSJ algorithm iteratively minimizes the off-diagonal elements of the matrix expressed in

The Jacobi transformations work by performing a sequence of orthogonal updates of matrix described bywhere is the processed matrix at iteration and is a Jacobi matrix at iteration .

With being “more diagonal” than its predecessor , the off-diagonal values are compared to a threshold in order to decide whether the Jacobi algorithm converged or not. This threshold is proportional to the on-diagonal values of and is defined bywhere is a constant value.

The Jacobi matrix (named also given rotation) is defined by the following formula:

The rotation angle is chosen so that the off-diagonal elements of are equal to zero [7]. should then satisfy the following:Equation (16) can be simplified to Two cases arise:(i)If then is already diagonal. Considering in this case and (rather than and ) makes no change to . We have then (ii)If then (17) remains to be solved.If , we define the parameters and . Equation (17) becomesEquation (17) has the two roots:As a result, the cosine and sine values of the Jacobi matrix (see (15)) can be calculated as

With matrix being real symmetric and sized, the theoretical maximum number of iterations is thus equal to . In practice, the expression of is quite complex to implement; it involves a division and calculation of square root, which is resource consuming when implemented on embedded platform. should then be approximated, and the off-diagonal values of are consequently no longer equal to 0 from the first iteration.

According to [8], the parameter can be approximated to make the algorithm faster. The variation of as function of is given in Figure 3.

Figure 3: Variation of as function of .

It is easy to see that the error between and its approximated value is limited when is close to 0. The value converges to zero when goes to infinity. The approximation of t will then develop for positive values of .

Using Taylor expansion the approximation of is given by

Based on the piecewise approximation we get the following result:

The approximation error is reported in Figure 4. It is obvious that the approximation errors have been controlled within a precision almost equal to , knowing that around is very close to .

Figure 4: Approximation error of .

The Jacobi process is finished when the off-diagonal elements of the matrix become close to zero. Finally, the Jacobi transformation process determines the following:(1)The right-hand singular vector matrix is equal to the multiplication of successive matrix rotations by the transformation matrix applied to (see (11)):where with being the rotation angle of the Jacobi matrix at iteration .(2)The singular values matrix .(3)The left-hand matrix is deduced byIn Figure 6 is reported the flowchart of the precoding process.

3.3. Quantization of the Precoding Matrix Fed Back to the Transmitter

As described above, performing the Jacobi based SVD on the channel matrix leads to matrices: the diagonal matrix , which contains the square root of the singular values of the channel matrix, the left-hand decoding matrix , and the right singular vector matrix also called precoding matrix.

In the transmitter, the precoding operation is performed on each carrier and consists of multiplying the output signals by the precoding matrix . is constructed using the two parameters and fed back from the receiver. To do this, the two parameters are uniformly quantized as follows:where is the number of used to quantize and is the number of bits used to quantize .

4. Simulation Results

To evaluate the TSJ based SVD precoding/decoding process, we developed a MIMO communication system composed of the following:(i)A transmitter chain that contains a random source delivering random streams containing bits, a splitter module to split the data into two MIMO signals, a mapping module using 16-QAM modulation, and a phase shifter which introduces a angle to the signal transmitted via the second antenna; the transmitter chain is shown in Figure 5.(ii)A receiver chain that contains a channel estimation module, a MIMO decoder, a deshifter, and demapping, as reported in Figure 2.

Figure 5: Transmitter chain of the precoded MIMO PLC system.
Figure 6: The precoding process flowchart.

The frequency band of the transmitted signal is [10 MHz, 86 MHz]. Simulations are carried out for MIMO channel generated and based on the MIMO PLC model detailed in [9] and based on the Multiconductor Transmission Line theory (MTL) [10].

4.1. Validation of the Proposed Precoding Algorithm

The performance of the Jacobi transformation algorithm is evaluated according to the mean square error (MSE) between the computed eigenvalues denoted as , , obtained from the proposed Jacobi algorithm and the eigenvalues , , obtained from the SVD function of Matlab (see Figure 7) where is the antenna index. The MSE is defined by

Figure 7: MSE of the two eigenvalues and , respectively.

The transmitted streams constellations before and after precoding are presented in Figure 8.

Figure 8: Transmitter chain of the precoded MIMO PLC system.

The 16-QAM constellations undergo a specific rotation due to precoding matrix multiplication.

4.2. Evaluation of the MIMO System Performance

In this section, the MIMO system performance evaluation is carried out in terms of energy conservation, Bit error rate (BER) variation, and the effect of the number of and quantization bits.

4.2.1. Energy Conservation of the System

The aim here is to verify the correctness of the TSJ-SVD algorithm in the decomposition of the estimated channel matrix . The MSE is calculated between the coefficients computed using the proposed SVD algorithm denoted by and the real values for generated MIMO channels. The comparison results are shown in Figure 9.

Figure 9: MSE between and .

According to Figure 9, the maximum value of MSE for the 3 different MIMO channels is below . The same results are found for other matrix coefficients. This undeniably proves that the SVD algorithm based on Jacobi transform is energy conservative. This is explained by the fact that all matrix transformations applied to channel matrix are unitary.

4.2.2. Bit Error Rate as Function of SNR

In this section is evaluated the BER for different values of SNR, for bit streams coming from both MIMO antennas. Simulations are carried out without any error correcting encoding/decoding. BER is compared to theoretical BER for uncoded 16-QAM.

According to Figure 10, BER behavior of our MIMO system is close to theoretical BER. This proves that no additional noise is brought by our MIMO precoding/decoding processing.

Figure 10: BER as function of SNR for bit streams from MIMO ports 1 and 2.
4.2.3. The Error Vector Magnitude (EVM) as a Function of Quantization Bits

As described above, the receiver feeds back the two quantized parameters and to the transmitter in order to make the latter construct the precoding matrix . In this section we study the effect of the number of quantization bits on the system performance. Simulations are carried out without adding any additive noise in order to only see the quantization noise effect. For evaluation purpose, we considered the constellation error vector magnitude (EVM) criteria. The EVM reflects the error between the received constellation after equalization and the original normalized 16-QAM constellation. In Figure 11 is reported the EVM (95th percentile point, which is the value where of the individual symbol EVM values are below that point) as a function of the number of quantization bits, calculated on the two MIMO streams. Each EVM point is calculated for constellations.

Figure 11: Effect of the number of quantization bits on EVM.

We observe that the EVM converges to when the number of quantization bits becomes high. In this case, the received constellation becomes closer to the original one. The EVM decreases below from a quantization number equal to .

5. Conclusion

In this paper is described a method for linear precoded MIMO PLC system based on TSJ-SVD algorithm. A simplified ZF equalizer is presented. The precoding matrix is compressed into 2 phase values which are quantified before being fed back from the receiver to the transmitter.

The proposed TSJ-SVD outputs were revalidated through comparison to Matlab SVD. Singular values , , calculated from SVD and Matlab were juxtaposed.

The MIMO system performance was finally evaluated in terms of precoding matrix energy conservation, BER as function of SNR, and effect of quantization bits on EVM. It is proven that without error correcting code the proposed system BER is close to theoretical BER meaning that no additional noise is brought by our precoding/decoding processing. Also, based on the EVM 95th percentile point criteria, it was shown that the performance of the MIMO system is better for large number of quantization bits and that the EVM decreases below from a quantization bits number equal to .

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


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