Wireless Energy Transfer

Question: Describe about a Report on Wireless Energy Transfer? Answer: Wireless Communication System and Shannon's capacity formula and Nyquist criterion Shannons capacity The rate of transfer of information in a communication channel is represented by Shannons capacity criteria of a channel. Capacity of a channel is the maximum rate at which data can be transmitted. Theorem of ShannonHartley represents the maximum transformation rate of information in presence of noise for a specific set of bandwidth over a communications channel. Data communication can be explained very well in the words as how fast the data can be sent in form of bits in a second over the channel. Three different features of Data rate are given as follows: Available Bandwidth Signalling level used by the user Signal to noise ratio of Channel The criterion of Shannon capacity states that the capacity of channel represented by C, also called the upper limit of information transmission rate represented by R is denoted by the formula of Shannon capacity for the signal having power S and additive white Gaussian noise power N of an analog communication channel can be represented by the formula: Where C = Capacity of the channel (bits per second) B = Bandwidth (in Hertz) S = Average of the total signal; received powerover the specified bandwidth (in Watts or squared value of volt) N = Average of total noise power for the indicatedbandwidth, S/N = Signal-to-Noise ratio (SNR) Shannons Capacity criterion in wireless communication Maximum capacity of a channel can be represented as the maximum data transmission C rate over a channel. Data transmission rate should always be less than channel capacity to avoid errors and improve the probability of transmission. the information rate should be R C. For the data rate greater than capacity error in the channel can- not be avoided. So the reversal of the Shannons theorem is also valid if R C. With the change in channel bandwidth the data rate also changes; increased bandwidth changes the capacity of channel as well as the information rate. Increment in signal to noise ratio SNR improves the channel capacity and prevents the errors due to noise. Nyquist criterion Nyquist rate gives the upper bound of the data transmission rate. Nyquist Criterion states that the rate at which signal is sampled should be greater than the maximum rate of the signal, also called the Sampling theorem. In the wireless communication system Nyquist criterion is utilized to remove aliasing. Numerical Example Suppose we have a channel that is affected by an extreme noise and the value of the signal to noise ratio for this channel is 0. Means the noised is strong and the signal is very weak in comparison to noise. For this condition the channel capacity C can be considered as: The solution shows that the capacity of the given channel is 0 with respect to the bandwidth. In other words, data reception can-not be performed through this channel. Suppose the given SNRdB is 46 dB and 5 MHz is the channel bandwidth . The capacity of channelcan be consideredhypothetically as: Explanation (b) Matlab Program for Shannons Theorem SNR (dB) 10 20 30 40 Bandwidth (MHz) 10 10 5 5 Data Rate /Capacity (Mbps) 3.46 6.69 4.98 6.64 Number of Signalling Levels 3 10 32 100 Channel capacity depends on Bandwidth. With an increment in BW channel capacity also increases. From the mathematical analysis it is clear that with the change in SNR channel capacity also changes. It can be concluded that with increasing SNR and reducing BW the channel capacity can be maintained at an appropriate level. References George, J. ,2013. Future Proof. How Wireless Energy Transfer Will Kill the Power Cable. MaximumPC. Higgins:, J. R., 1985. Five short stories about the cardinal series, Bulletin of the AMS 12. Marks II,, R. J., 2009. Handbook of Fourier Analysis and Its Applications, Oxford University Press. Unser, M. , 2000. Sampling-50 Years after Shannon. Proc. IEEE, 569-587. Robust demand for mobile phone service will continue, UN agency predicts., 2010. UN News Centre . Jerri, A., 1977. The Shannon Sampling TheoremIts Various Extensions and Applications: A Tutorial Review. Proceedings of the IEEE. Linebaugh, K., 2010. Medical Devices in Hospitals go wireless. Online.wsj. The Wall Street Journal. Meijering, E., 2002. A Chronology of Interpolation From Ancient Astronomy to Modern Signal and Image Processing. Proc. IEEE. Mishali, M., Eldar, Y. C., 2009. Blind Multiband Signal Reconstruction: Compressed Sensing for Analog Signals. IEEE Trans. Signal Processing 57. Nyquist, H., 2002. Certain topics in telegraph transmission theory. Reprint as classic paper in: Proc. IEEE, 617-644.