NOISE IN TELECOMMUNICATION

Prepared by

Md. Murtoza Ali Quader

&

Md. Mukit Alam

Subject:- ETE-423

North South University

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INTRODUCTION:

Electronic noise is a random fluctuation in an electrical signal, a characteristic of all electronic circuits. Noise generated by electronic devices varies greatly, as it can be produced by several different effects. Thermal noise and shot noise are inherent to all devices, while other types depend mostly on manufacturing quality and semiconductor defects.

The noise level in an electronic system is typically measured as an electrical power N in watts or dBm, a root mean square (RMS) voltage (identical to the noise standard deviation) in volts, dBμV or a mean squared error (MSE) in volts squared. Noise may also be characterized by its probability distribution and noise spectral density N0(f) in watts per hertz.

A noise signal is typically considered as a linear addition to a useful information signal. Typical signal quality measures involving noise are signal-to-noise ratio (SNR or S/N), signal-to-quantization noise ratio (SQNR) in analog-to-digital coversion and compression, peak signal-to-noise ratio (PSNR) in image and video coding, Eb/ N 0 in digital transmission, carrier to noise ratio (CNR) before the detector in carrier-modulated systems, and noise figure in cascaded amplifiers.

In communication systems, the noise is an error or undesired disturbance of a useful information signal, introduced before or after the detector and decoder. The noise is a summation of unwanted or disturbing energy from man-made and natural sources. Noise is, however, often distinguished from interference, (e.g. cross-talk, deliberate jamming or other unwanted signals from specific transmitters), for example in ratio signal-to-noise plus interference (SNIR). Noise is also typically distinguished from distortion, which is an unwanted alteration of the signal waveform, for example in the signal-to-noise and distortion ratio (SINAD). In a carrier-modulated passband analog communication system, a certain carrier-to-noise ratio (CNR) at the radio receiver input would result in a certain signal-to-noise ratio in the detected message signal. In a digital communications system, a certain Eb/N0 would result in a certain bit error rate (BER).

While noise is generally unwanted, it can serve a useful purpose in some applications, such as random number generation or dithering.

In common use, the word noise means any unwanted sound. In both analog and digital electronics, noise is an unwanted perturbation to a wanted signal; it is called noise as a generalisation of the audible noise heard when listening to a weak radio transmission. Signal noise is heard as acoustic noise if played through a loudspeaker; it manifests as 'snow' on a television or video image. Noise can block, distort, change or interfere with the meaning of a message in both human and electronic communication.

In signal processing or computing it can be considered unwanted data without meaning; that is, data that is not being used to transmit a signal, but is simply produced as an unwanted by-product of other activities. "Signal-to-noise ratio" is sometimes used informally to refer to the ratio of useful information to false or irrelevant data in a conversation or exchange, such as off-topic posts and spam in online discussion forums and other online communities, In Information

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Theory, however, noise is still considered to be information. In a broader sense, film grain or even advertisements encountered while looking for something else can be considered noise. In biology, noise can describe the variability of a measurement around the mean, for example transcriptional noise describes the variability in gene activity between cells in a population.

BACKGROUND:The ITU-R 468-weighting curve (originally defined in CCIR recommendation 468) is widely used when measuring noise in audio systems, especially in the UK, Europe, and former countries of the British Empire such as Australia and South Africa. It is less well known in the USA where A-weighting has always been used.

While most audio engineers are familiar with the A-weighting curve, which was based on the 40 phon equal-loudness contour derived initially by Fletcher and Munson (1933) the later CCIR-468 weighting curve, now supported as an ITU standard is less well known outside of the UK and Europe. Originally incorporated into an ANSI standard for sound level meters, A-weighting was never specifically intended for the measurement of the more random (near-white or pink) noise in electronic equipment, though it came to be used for this purpose. It is now known than the human ear responds quite differently to random noise, and it is this difference that gave rise to the 468-weighting, which became widely used by broadcasters throughout Britain, Europe, and former British Commonwealth countries, where engineers were heavily influenced by BBC test methods.

Developments in the 1960s, in particular the spread of FM broadcasting and the development of the compact audio cassette with Dolby-B Noise Reduction, alerted engineers to the need for a weighting curve that gave subjectively meaningful result on the typical random noise that limited the performance of broadcast circuits, equipment and radio circuits. A-weighting was not giving consistent results, especially on FM radio transmissions and Compact Cassette recording where Preemphasis  of high frequencies was resulting in increased noise readings that did not correlate with subjective effect. Early efforts to produce a better weighting curve led to a DIN standard that was adopted for European Hi-Fi equipment measurement for a while.

Experiments in the BBC(The British Broadcasting Corporation, usually referred to by its abbreviation as the "BBC", is the longest established and largest broadcaster in the world) led to BBC Research Department Report EL-17, The Assessment of Noise in Audio Frequency Circuits , in which experiments on numerous test subjects were reported, using a variety of noises ranging from clicks to tone-bursts to 'pink' noise. Subjects were asked to compare these with a 1 kHz tone, and final scores were then compared with measured noise levels using various combinations of weighting filter and quasi-peak detector then in existence (such as those defined

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in a now discontinued German DIN standard). This led to the CCIR-468 standard which defined a new weighting curve and quasi-peak rectifier.

The origin of the current ITU-R 468 weighting curve can be traced to 1956. The 1968 BBC EL-17 report discusses several weighting curves, including one identified as D.P.B. which was chosen as superior to the alternatives: A.S.A, C.C.I.F and O.I.R.T. The report's graph of the DPB curve is identical to that of the ITU-R 486 curve, except that the latter extends to slightly lower and higher frequencies. The BBC report states that this curve was given in a "contribution by the D.B.P. (The Telephone Administration of the Federal German Republic) in the Red Book Vol. 1 1957 covering the first plenary assembly of the CCITT (Geneva 1956)". The BBC report states "this characteristic is based on subjective tests described by Belger." and cites a 1953 paper by E. Belger.

Dolby Laboratories took up the new CCIR-468 weighting for use in measuring noise on their noise reduction systems, both in cinema (Dolby A) and on cassette decks (Dolby B), where other methods of measurement were failing to show up the advantage of such noise reduction. Some Hi-Fi column writers took up 468 weighting enthusiastically, observing that it reflected the roughly 10dB improvement in noise observed subjectively on cassette recordings when using Dolby B while other methods could indicate an actual worsening in some circumstances, because they did not sufficiently attenuate noise above 10kHz.

IMPROVEMENT:In modern technology, some developments have been made to suppress noise in communication system. Some of them are listed below:

Distributed noise reduction circuits in telecommunication system connector

A connector for telecommunication systems has input terminals and output terminals arranged in ordered arrays and connected by a circuit having a plurality of conductive paths connecting the respective input and output terminals. The circuit cancels crosstalk induced across adjacent connected terminals by sets of sections. Two of the paths have one set of sections connected in series and spaced from each other between the input and output terminals. Another pair of paths have another set of sections connected in series and spaced from each other between the input and output terminals. Each section of the two sets forms a noise reduction circuit providing a reactive coupling between the respective paths which is greater than adjacent portions of the paths spacing the sections of the two sets.

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Low crosstalk noise connector for telecommunication systems

A connector for communication systems includes first and second interfaces electrically coupled by a circuit. The first interface has first, second, third and sixth primary terminals arranged in order in a first ordered array. The second interface has a plurality of secondary terminals arranged in a second ordered array. The circuit couples the primary terminals to the respective secondary terminals and cancels crosstalk induced across adjacent terminals. The circuit includes conductive traces connecting the respective primary and secondary terminals. Sections of first and third paths are in relatively close proximity to provide a first reactive coupling between those two paths. Sections of the first and sixth paths are in relatively close proximity to provide a second reactive coupling between those two paths. The sections of the conductive paths have lengths, widths and spacings to cancel the crosstalk induced at the terminals.

Enhanced capability in digital communication is recognized as a pivotal element in a future economy, education, commercial and social activities. The role of fibre communications, currently providing for a healthy fraction of the total information traffic, will considerably increase in the near future. This will be driven by the introduction of and advances in new services such as videophony, video on demand, computer based banking, high speed computer communications and other elements of economical and social infrastructure. Light-wave fibre communication systems form the backbone of the present day high-speed data transmission links. The information is encoded and transmitted through an optical channel as a digital bit stream. One of the principal issues to be addressed by the researchers and system designers is to estimate the probability of errors (bit error rate) occurring in such bit stream due to the intrinsic noise in a transmission system. Noisy communication links need special solutions - primarily special coding techniques to minimise noise/errors in the information bit-stream. Knowledge of noise statistics of nonlinear fibre channels is crucial for the design of efficient and adequate coding techniques and practical realizations of coding devices. Therefore, novel mathematical, theoretical and numerical approaches are required to understand error statistics in order to create advanced future communication systems. Another challenging and exciting task is to find the way to use the always present intrinsic noise to our advantage. This seems like quite a paradoxical idea: for generations noise has been considered as nuisance that must be eradicated at all cost. However in certain nonlinear systems, including electronic circuits and biological sensory systems the presence of noise can enhance the detection of weak signals. The phenomenon is known as stochastic resonance and is of great interest to electronic instrumentation and telecommunications. The question arises whether this phenomenon can be used in communications and can the new devices be constructed based on this effect. In the current project it is first planned to apply a novel simulation technique for studying the statistics of the signal output in optical fibres. This approach will allow to obtain precise and explicit description of the statistics of the transmitted signal. These in turn, are crucial for

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determining the bit error rate and system performance. The advantage of the proposed algorithm is that it allows one to model the statistics of extremely rare events, something, which is utterly impossible to achieve with the conventional Monte Carlo techniques that are widely used at present. Therefore it is expected that with this new powerful tool we will be able to calculate numerically the error probabilities, which are of the order of 1 error event per 1,000,000,000 transmitted bits. Our method will allow us to obtain these results without significant CPU time penalty, which makes the proposed approach very practical. This technique is crucial for the correct estimation of the bit error rate and, therefore, the overall system performance. We will then develop the techniques that will help to improve the quality of detection reducing the probability of mistaking logical "ones" for "zeros" and vice versa. The final and most challenging stage of the project is to propose and develop nonlinear electronic devices that make use of the phenomenon of stochastic resonance. During this stage of the project we will use the data about signal statistics collected at the initial stages of the project by Monte Carlo simulations.

CONCLUSION:In this term paper, we tried to know basic facts about noise. We came to know how it affected the telecommunication system. We learned about the distorted output we got because of noise. Then, we went back into history to know about the researches that took place to prove the presence of noise and its consequences in our communication system. After that, we studied about the technical development and the physical implementation that has been made to suppress noise from the telecommunication system.

COMMENT:After going through all the researches, we have come to a conclusion that the amount of noise has been minimized and the efficiency of the communication system has increased greatly. Still, noise will always be present in the communication system and it cannot be suppressed completely.

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Reference:1) http://en.wikipedia.org/wiki/Noise_%28electronics%29 2) gow.epsrc.ac.uk/ViewGrant.aspx?GrantRef=EP/E055672/13) www.freepatentsonline.com/5931703.html 4) www.faqs.org/patents/app/200802793665) www.absoluteastronomy.com/.../ITU-R_468_noise_weighting

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