Linear Predictive Coding

Linear predictive coding(LPC) is defined as a digital method for encoding an analog signal in which a particular value is predicted by a linear function of the past values of the signal.

Human speech is produced in the vocal tract which can be approximated as a variable diameter tube. The linear predictive coding (LPC) model is based on a mathematical approximation of the vocal tract represented by this tube of a varying diameter. At a particular time, t, the speech sample s(t) is represented as a linear sum of the p previous samples.

The LPC is the linear predictive filter which allows the value of the next sample to be determined by a linear combination of previous samples.

LPC is frequently used for transmitting spectral envelope information, and as such it has to be tolerant of transmission errors. Transmission of the filter coefficients directly (see linear prediction for definition of coefficients) is undesirable, since they are very sensitive to errors. In other words, a very small error can distort the whole spectrum, or worse, a small error might make the prediction filter unstable.

 LPC is generally used for speech analysis and resynthesis. LPC synthesis can be used to construct vocoders where musical instruments are used as excitation signal to the time-varying filter estimated from a singer's speech.

LPC predictors are used in Shorten, MPEG-4 ALS, FLAC, and other lossless audio codecs

 

(This is compilation on LPC)

 

To view .ppt on this topic visit http://www.slideshare.net/sdd2311/lpc-29316427

 

 

References:

[1] "Linear Predictive Coding" by Jeremy Bradbury, December 2000.

[2] http://en.wikipedia.org/wiki/Linear_predictive_coding

[2] www.ieee.com

 

Need for Energy Conservation in Wireless Technology

Since 2006, data traffic on wireless networks has grown by approximately 400% and is expected to continue to increase rapidly in the coming years. The widespread use of complex, spectrum efficient techniques to support such high data volumes, the demand for higher data rates and the ever-increasing number of wireless users translate to rapidly rising power consumption. Currently consuming 3% of the energy and causing 2% of the CO2 emissions globally, the Information & Communication Technology (ICT) industries are facing an increase in associated energy consumption of 16-20% per year. Furthermore, the energy costs for mobile operators can be as high as half of their annual operating budgets. The foregoing considerations highlight the urgent need for focusing on energy efficiency.  The recent phenomenal growth of data services in cellular mobile networks has exacerbated the energy consumption issue and is forcing researchers to address how to design future wireless networks that take into account energy consumption constraints.

Due to the anticipated tenfold increase of traffic requirements in the next generation mobile networks and the costs associated with those, as well as the requirement for a significant reduction of the carbon footprint of such systems. Energy-efficient scheduling is a topic that has been discussed widely within wireless sensor networks.

In developing countries direct electricity connections are not readily available, so Vodafone, for example, use in excess of 1 million gallons of diesel per day to power their network. Mobile communications thus contributes a significant proportion of the total energy consumed by the information technology industry. Recent analysis by manufacturers and network operators has shown that current wireless networks are not very energy efficient, particularly the base stations by which terminals access services from the network.

The energy consumed by a Wireless Network is mainly in the following criteria:

·         Energy consumed by the network in operation.

·         Embedded  emissions  of  the  network  equipment,  for  example,  emissions  associated with  the manufacturing and deployment of network equipment.

·         Energy  consumed  by  mobile  handsets  and  other  devices,  when  they  are  manufactured, distributed and used, as well as their embedded emissions.

·         Emissions associated with buildings run by mobile operators, and emissions from transport of mobile industry employees.

                 Figure:1 Direct Emission of Mobile Industry 2009, 245 M tones of CO₂

 

(This is compilation of data acquired on the topic)
To view related ppt, visit
http://www.slideshare.net/sdd2311/green-radio-final

References:

 

   [1]         www.mobilevce.com

   [2]         www.trai.gov.in

[3]         www.greenpeace.org