Power Line Channel & Transmission of Compressed Data Research Paper

Introduction

Transmission of compressed data over power line channels has attracted considerable interests in the research community. Recent trends in communication have seen strong needs for high data transmission, where there is significant increase in the transmission of image data. Typically, various errors have been recorded during the transmission process, and this is because image data require large amount of information to be transmitted, and high energy communication requirements. These constraints in transmission normally lead to multi path fading, high bit error rates, and a degradation in the qualities of data received. Due to various problems of transmission of data that pose significant constraints on the communication system, various channel coding have been proposed (Athanasiadis, Lin, Hussain). Previous works have revealed that data compression can provide effective data transmission over power line channel.

This paper extends this line of research by examining the effectiveness of compressed technology in transmission of data over power line channels. Compression technology in the transmission of mage data has been in the heart of modern communication products, and compression algorithms have enhanced key compression technologies such as JPEG2000, and MPEG, and this strategy have enhanced transmission in order to suit various techniques in communication parameters. (Athanasiadis, Lin, Hussain). Studies reveal that compressed data transmission over power line is quicker and is an effective method on image transmission, where it takes lesser space than uncompressed data. Data from the source minimize the number of bits transmitted in the power channel, and transmissions by compressed data offers security across networks. (Schroeder, Ackerman).

Data compression is the process of encoding information into fewer bits to enhance effective use of space in data transmission. Typically, transmitting compressed data through power line involves two encoding schemes sender and receiver, where compressed data is effective when receiver understands the decoding data. The compressed data is effective for transmitting data than encoding scheme because of efficient faster methods of transmitting data, and reduction of the use of expensive resources. For example, transmitting video image by power line through uncompressed data may be expensive, and slow, however compressed video transmission requires less storage space (Mahoney). Typically, transmission speed of PLC devices can support 400 Mb/s, and this has proven support multiple multimedia purposes. (Gulak, Kschischang ). For example, high speed PLC can transmit several hundred of megabits per second, and the technology has proven to serve as effective system for transmitting high speed data. (Sheybani , Rashidi). For development of power line systems, and choosing suitable transmission methods, there is need for detailed knowledge of channel properties, and channel capacity. Studies have revealed that efficient approach of data compression and efficient transmission are not common phenomenon. For example, considering compression in form of y = Ax, where A = is a k × n, the vector in compressed data show that it is easier to transmit data than uncompressed data. (Haupt et al).

The rest of the paper is organised as follows:

First, the paper provides systems design in power line channel as a medium of communication to transmit compressed data. The parameter used in system design is to allot coding in the compressed data stream to remove redundancy compressed data in order to increase the effectiveness of data transmission. (Sanaei, Ardakani).

Moreover, the paper provides mathematical approach to the effectiveness of power line channel as medium to transmit compressed data through channel model and signal.

In addition, the paper provides experimentation of data transmission of still data. This is done by conducting test experiment to ensure that the research obtains satisfactory results.

Finally, the study provides summary of the findings.

Power line Communication

Concept of Power Line Communication is not new in the engineering cycle, PLC has been applied for numerous application including power utilities. Although, in the past, the use of PLC was purposely for sending control messages through medium and high voltages lines, presently, PLC has become an effective method of transmitting data. Given the proportion of energy consumed, and amount of space that data occupy during data transmission, data compression has become an effective method of transmitting data because of the amount of space that can be save during transmission. Standard compression algorithm is aimed at saving storage, and at the same time ensuring data integrity, where compression ratio achieved can reduce the number of bits during transmission. Typically, with data compression algorithms, reduction of code size foot prints, and dynamic reduction of memory usage can be achieved and eliminating unnecessary communication through data compression. (Ribeiro et al, Sadler, Martonosi, Zirkind)

For example, transmission of 32-bit will require more than 1000 times of energy consumption than transmission of single bit over power line, and this simple analogy reveals that the increase in the bits to be transmitted over power line requires more energy consumption. Thus, it is beneficial to reduce data before effecting the transmission. (Barr, Asanovic, Sadler, Martonos).

System design

In this section, the paper employs system and communication models to analyse the data transmission of compressed data. The objective of system model is to analyse the sequence of power line communication of transmission in digital information. Typical, the digital transmission system communicate at high bit rates. (Lund University).

The transmission of data takes certain procedure before reaching the output. From fig 1, data are transmitted from source to source encoder, and it is from source encoder that data are transmitted at a bit rate Rb. Although, there may be error when measuring the level of performance and the probability of error bit of this information is denoted as Pb, the error bit can occur when bits incorrectly receive information. To reduce probability of bit error, the channel encoder increase the data redundancy, and when there is possibility of appearance of errors, the channel decoder can use information in the bit stream to detect and correct the errors. Typically, the redundancy of most data necessitates the need to compress data to minimise the amount of bits transmitted over the communication channel. Thus, at the receiver source coding, the compressed data will either become the replica of the data from the source data or may become a distorted version. The compression will increase if the receiver source decoder is not exact copy of the data transmitted over the channel. (Lund University).

There are different techniques to achieve reliable high data communication from the receiver. (Baudis, Crussiere).As illustrated in fig 2, power line communication channel consists of communication between transmitter and receiver. The transmitter ensures effective communication to coupling circuits, and the communication system to the coupling circuits ensures protection of 50Hz that is used for power distribution. The coupling circuits also ensure that the major part of transmitted and receiver signals certify their use for communication.

As illustrated in fig 2, b represents information bits rate Rb, and c represents coded binary symbols that encode output. ( Lund University).

Typically, data transmission through the power line may not be efficiently carried out unless the data are compressed to ensure that there is reduction of physical size of data. To compressed data, there is need for suitable use of algorithm to optimize data. Data compression depends on data to be compressed; basically, compressing digital file is different from compressing audio file. (Kioskea.net). Compression of a picture involves data encryption, and implementing encryption algorithms require evaluation between processing time, and compression ratio to achieve data integrity, and quality of data. For example, the image of data using JPEG compression can achieve compression ratio between 1:17 and 1:23, and reduction in file size can produce minimum of 25% and a maximum of 40%. (See fig 3) (Zirkind).

Thus, the advantage of compressed data is that it requires storage of large data into less space, and this lead to efficient use of available disk space, and its algorithm is faster. (Praveen, Gupta, Mona). There are several strategies to design compressed schemes, the use of distributed coding techniques is known for designing compressed scheme. In addition, in-networking and compression may exploit exchange of information and dependencies between networks of data. However, it’s designing strategies and effective implementation of algorithms can be challenging to system designer. The challenging problem is due to its dependencies on sophisticated communication and node process capabilities. (Haupt et al).

The best technique of designing compressed data is technique proposed by Haupt, called data-aware where its implementation to make use of minimum amount of storage space. Illustrating its paradigm requires influx of data sets which represent n suffixes of T. These suffixes are arranged in lexicographical order to allow fast search queries. The theory of compress sensing reveals that sufficient compressed signal can be accurately recovered for a small number where model y=Ax, where matrix A is a k x n CS. Thus, the illustration of CS theory reveals the promising results. Typically, data transmission requires electric signal carrying information to be transmitted from source coding , and during transmission process, the source encoder unpack the data to minimise the amount of data to be transmitted over the power line channel. (See fig 1). (Lund University). The overall coding system takes R.N bits from source encoder and produces N bits where N coded is the power line channel. It should be noted that in each power line channel has / bits represented on of the 2/points (Saneei, Ardakani). Thus, to ensure reduction of error probability, diversity techniques is used to transmit information to different channel. For instance, if information is bad, the diversity technique will ensure the transmission of data when channel is better. (Lund University).

In the receiver end, decoding process starts where encoder is required. At the receiver end, this process is reversed. Although, there are several iterative decoding algorithms that can be applied to decoding process, effect of overall performances of the system is to reduce frequency tone to zero in order to reduce the overall complexity. For example, the 1000 bit channels can be reduced to 800 bit channels with capacity of 05. The complexity reduction because of the coding solution. (Sanaei, Ardakani).

Further mathematical approach is needed for discussion of compressed data transmission.

Mathematical approach

This section provides mathematical approach to compressed data transmission over power line. To decode algorithms, data could be generated in n-dimensional vectors. The observation matrix A of y = Ax =0 and the two distinct m-sparce signal that is x and xo can be compressed to same data where Ax= Axo.. It should be noted that ak x n sensing matrix with unit rows, and can be represented by Pn =1 A2 ,j = 1 for i = 1, 2,… , k), and is used to satisfy order s whenever

(1 − _s) k nkxk22 _ kAxk22 _ (1 + _s) k nkxk22

Given a vector y = Ax, the unknown m-sparse signal x can be exactly as the unique solution as argmin z kzk1 subject to y = Az,……….. (1) (Haupt et al). To effect the overall performances of the systems, low-capacity bit channels is essential for effective capacity of the system where approach employed in the reduction of frequency tone to zero, and signal energy in active frequency. The approach is to reduce the overall complexity of 1000 bit channels of average capacity. Thus coding solution will be effective if K=4 and the capacity selected to be K = 4 and the capacity ranges for sub channels are selected to be [0.2, 0.4), [0.4, 0.6), [0.6, 0.8), and [0.8, 1). On a 64-QAM signalling, where capacity ranges are [1.2 dB, 6.5 dB), [6.5 dB, 10.8 dB), [10.8 dB, 14.8 dB), and [14.8 dB, +∞). From the distribution of the SNR (Figure 2), it can be easily found that

γ1 = 0.3364, γ2 = 0.2949, γ3 = 0.2022, and γ4 = 0.1665.

Typically, Monte Carlo’s stimulation provides accurate analysis for a DMT system where distribution is used in density evolution. It should be noted that all codes are designed to ensure that 10-7 error rates are not more than or less than 400 iterations in implementation of discrete density evolution. Meanwhile, to choose 400 iterations and 11-bit decoding are applicable to maximise node degree of 10 to optimized degree distribution of channel which value is:

ρ = and Λ = .

The code design is used to simplified code process by more than 96% of average capacity of bit-channels where 0.5077 bit per channel is achieve, and K=4 is used to simplified code. Thus, with optimization of conventional code, the overall result is ρ = and λ = . This code has a rate of R = 0.4129 which is no more than 81.3% of the capacity of the channel. (Sanaei, Ardakani). Typically, the algorithms in computing compression is the distributed code string that is denoted by distribution p, and in arithmetic code x, expressed binary fractions 0, 1 where p (<s) <= x < p (<=s), where p (<s) represents cumulative probability les than s. This provide predictions of p(s) = p(s₁s₂…s_n) = p(s₁)p(s₂|s₁)…p(s_n|s₁…s_n-1), and bounds on x after each symbol is read with outputting the bits of x as soon as they are known. It should be noted that Arithmetic coding is used in context mixing algorithms. (Mahoney).

The overall results reveal that compressed data transmission is more effective than uncompressed data as discussed from the results from the stimulation.

Experimental Results and discussions

This section discusses the experimental results of data compression over power line. The computer stimulation was carried out using MATLAB software.(See MATLAB code at appendice). With reference to fig 3, the use of wavelet transform (DWT) can transform image compression application. For example, wavelet transform coding has been accepted image compression using transform coding and this technique can process JPEG 2000 image with benefits of transforming coding to include high energy compaction capability, and reduction of image distortion. The wavelet transforms better image quality especially for large compression ratios. (Athanasiadis, Lin, Hussain). Typically, the communication model of data transmission of compressed data in fig 3 also reveal that when user inputs data , data passes through stages of formatting, and transmitted over power line channel before reaching receiver model. This is a stage where data transmit to Entropy Decoder before reaching data reconstruction, where at this stage the data are produced at output stage. With stimulation, overall results of compressed data produced at the output stage in the transmission communication model are presented in table 2, and fig 4.

More result recorded during stimulation are as follows:

Speed of transmission: The experimental results reveal that better results can be achieved from transmission speed of compressed data. For example, this paper employs techniques of finding the compression ratio and compression speed. The technique employed is by comparing the uncompressed data and the same document compressed in JPEG file. The results reveal that compression mode achieve faster rate that uncompressed mode (See Table 1)

Table 1: Compression speed of 15KB (Average (MBps)

Energy saving: The paper is able to record saving of energy when sending data in compressed form. The important remark is that compression is beneficial to the network. For example, Sadler and Martonosi reveal that compression node does not consume down stream energy, compression algorithm performs well for all of the datasets and energy can be saved when transmitting compressed data than transmitting uncompressed data.

The overall results reveal that transmission of power line model of compressed data can achieve a maximum bits rate close to a gigabit per second. (Science News). Moreover, there can be better use of connection of bandwidth in a computer network. The image data can not lose essential nature of data and the great deal of space are recorded with compression methods of data size over power line. It should be noted that in transmitting compressed image, there is minimal degradation of picture quality. (Mahoney). It should be noted that data compression is largely advantageous because of its cost saving especially in data processing. Many large volumes of data can be manipulated for many engineering application, and run length encoding can be used to accomplish number of possible attribute values.

(Christopher, Margaret).

From Table 2 and Fig 4, the results of stimulation reveal that transmission of compressed data is shown to be very effective while transform-based compression is well developed in image processing. Table 2 and Fig 4, and fig 5 are able to demonstrate that there is considerable reduction in files’ size with data compression, and this enhance degree of fast data transmission over power line.

Table 2 : Compression performances from stimulation results.

Additionally, compressed data contain lesser volume of space than uncompressed data, and the times taken to transmit compressed data are lesser compared to uncompressed data.

Impulsive noise: impulse noise is characterised of power line transmission bandwidth where background of impulse modulation convey information into stream of sequence. of Impulsive noise reveals that there is presence of impulsive noise generated from by the frequency of power line channel. However, the complexity of the noise was reducing with spreading with spreading of frequency.( Tonello).

Conclusions

The paper presents the transmission of data over power lines. The paper reveals that transmission of compressed data is effective compared to non-compressed data where the transmission of compressed data over power line can achieve minimum time and space in data transmission. The strategies developed for data transmission reveal that there can be optimisation of use of energy and time. ( Fu, Modiano, Tsitsiklis). Efficient use of compressed methods depends on the type of data to be transmitted where image can not be transmitted the same methods with audio methods, and the amount of time needed depends on the strategic used in transmitting data over channel. (kioskea.net).

Typically, the evaluation of algorithms in data compression can be susceptible to error, and further research is needed on the elimination of errors in algorithms during data transmission. In addition, heavy computation is essential for graphic application due to the high speed requirements involved in transmission.

This paper enhances the knowledge of governments, scholars, and business organisations on transmission of compressed data over power lines.

Work cited

1. Athanasiadis, Tasso, Lin, Kevin H, Hussain, Zahir M, Transmission of Compressed Multimedia Data over Wireless Channels using Space-time OFDM with Adaptive Beam forming, IEEE Xplore, 2008.
2. Ackerman, Kevin Wade, Timed Power Line Data Communication, A Thesis Submitted to the College of Graduate Studies and Research, Degree of Master of Science, Department of Electrical Engineering University of Saskatchewan Saskatoon, Saskatchewan, Canada. 2005.
3. Barr, Kenneth, C, Asanovic, Krste, Energy-Aware Lossless Data Compression, ACM Transactions on Computer Systems, 2, 3: pp 250–291, 2006.
4. Baudis, Jean-Yves, Crussiere, Matthieu, Resource Allocation with Adaptive Spread Spectrum OFDM Using 2D Spreading for Power Line Communications, Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing 2007, Article ID 20542.
5. Fu, Alvin, Modiano, Evtan, Tsitsiklis, John, Optimal Energy Allocation for Delay-Constrained Data Transmission over a Time-Varying Channel, IEEE INFOCOM 2003.
6. Gotz, M, Rapp,M, Dostert, K, Power line channel characteristics and their effect on communication system design, Communications Magazine, IEEE (42), 4, pp. 78 – 86, 2004.
7. Gulak, Esmaillian, T, Kschischang, F, R, Acoustics, Speech, and Signal Processing, IEEE International Conference on 5, (2000), pp:2953 – 2956.
8. Haupt Jarvis, et al, Compressed Sensing for Networked Data, Draft, Mcgill Electrical and Computer Engineering, 2007.
9. Kioskea.net, Data Compression.
10. .Lund University, power-line communication over the low-voltage, Department of Information Technology, 1999.
11. Mahoney, Matt, Rationale for a Large Text Compression Benchmark
12. Matt Mahoney, Florida Institute of Technology, 2006
13. Paruchuri, Vamsi, et al, Securing Powerline Communications, Ordyn Technologies Private Ltd, 2007.
14. Praveen,B, Gupta, Deepack, Mona, Rajat, Design and Implementation of a File system with on-the fly Data compression for GNU/Linux, Department of Computer science and Engineering , India Institute of Technology , Kanpur, 2008.
15. Science News, Power Line Data Transmission Capacity: Bigger Than DSL Or Cable, Science Daily, 2005.
16. Ribeiro, Vida Moises, et al Advanced Signal Processing and Computational Intelligence Techniques for Power Line Communications, EURASIP Journal on Advances in Signal Processing, vol 2007, Article ID 45812.
17. Sadler, Christopher, Martonosi, Margaret, Data Compression Algorithms for Energy-Constrained Devices in Delay Tolerant Networks, SenSys, 2006.
18. Sadler, Christopher, Martonosi, Margaret, Energy Conservation Techniques in Mobile Delay-Tolerant Sensor Networks, Princeton University.
19. Sanaei, Ali, Ardakani, Masoud, LDPC Code Design for Nonuniform Power-Line Channels, Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing, vol 2007, Article ID 76146.
20. Schroeder, Wayne, Secure Compressed Data, from RB Data Security and Compression System, University of North Carolina Chapel Hill, 2003
21. Sheybani, E, Rashidi, N, Power Line Communication Denoising with Adaptive Wavelet Filters, A CTA Press, 2008.
22. Tonello, Andrea M, Wideband Impulse Modulation and Receiver Algorithms for Multiuser Power Line Communications, Journal on Advances in Signal Processing, 2007.
23. Zirkind, Givon, AFIS Data Compression: An Example of How Domain Specific Compression Algorithms Can Produce Very High Compression Ratios, ACM SIGSOFT Software Engineering Notes, 32 :6, 200

Appendices

Appendix 1: MATLAB code

Matlab routines

contour2.m

displtxt.m

displvar.m

drswitch.m

fontset.m

helpint.m

helpomp.2

incontr2.m

nansum.m

norm_qwt.m

def_sources.m

omp2.m

omp2auto.m

omp2gui.m

omp2int.m

qwt2.m

runomp2.m

sortomp2.m

sortwrt2.m

statns2.m

Data files in ASCII format

testdata.mat

testlevels.mat

testwght.mat

lat = holddata(1,:);

long = holddata(2,:);

press = holddata(3,:);

temp = holddata(4,:);

sal = holddata(5,:);

ptemp = holddata(6,:);

pdens = holddata(7,:);

oxy = holddata(8,:);

ph = holddata(9,:);

ni = holddata(10,:);