Flat fading is one in which all frequency components of a received radio signal vary in the same proportion simultaneously. There are two types of fading according to the effect of Doppler Spread. Slow fading When the coherence time of the channel is large relative to the delay constraint of the channel then slow fading will occurred.
The amplitude and phase change imposed by the channel can be considered roughly constant over the period of use.
The events such as shadowing, where a large obstruction such as a hill or large building obscures the main signal path between the transmitter and the receiver, causes the slow fading..
Fast fading When the coherence time of the channel is small relative to the delay constraint of the channel causes the fast fading. The amplitude and phase change imposed by the channel varies considerably over the period of use. Types of small scale fading There are many models that describe the phenomenon of small scale fading. Out of these models, Rayleigh fading, Ricean fading and Nakagami fading models are most widely used.
Rayleigh fading model: The Rayleigh fading is primarily caused by multipath reception. Rayleigh fading is a statistical model for the effect of a propagation environment on a radio signal. It is a reasonable model for troposphere and ionospheres signal propagation as well as the effect of heavily built-up urban environments on radio signals.
Rayleigh fading is most applicable when there is no line of sight between the transmitter and receiver. Ricean fading model: The Ricean fading model is similar to the Rayleigh fading model, except that in Ricean fading, a strong dominant component is present. Additive White Gaussian Noise Model: The simplest radio environment in which a wireless communications system or a local positioning system or proximity detector based on Timeof- flight will have to operate is the Additive-White Gaussian Noise AWGN environment.
Additive white Gaussian noise AWGN is the commonly used to transmit signal while signals travel from the channel and simulate background noise of channel. It is the basic communication channel model and used as a standard channel model.
The transmitted signal gets disturbed by a simple additive white Gaussian noise process. The bit error probability pe is the expectation value of the BER. The BER can be considered as an approximate estimate of the bit error probability. This estimate is accurate for a long time interval and a high number of bit errors. Open navigation menu. Close suggestions Search Search.
Theorem 4. Hence the algorithm described above and depicted by Algorithm 1 5. Numerical results converges to the global optimum.
In this section, the performance of the new reliability based The proof of this theorem can also be found in Appendix D. The methods were tested on a network task indicated with The source and BS.
The other algorithm is the directed diffusion Percentage of the nodes alive 0. Thus, it is hard to measure with. DD Assume the DD algorithm has already detected the optimal route 0. Let Pir denote the reliability probabil- 0. To ensure that the overall QjRj 0. If we decrease the value of jRjav , then the lifespan of WSN will in- 0 0 crease but at the price of decreasing the reliability of the packet Number of rounds, k transmission.
Conversely, if we increase the value of jRjav , then the reliability will be improved, and the lifespan will be decreased. Average delay of hops In our simulations, we have assigned values to the parame- 50 ters e.
One can see in Fig. The results are de- Fig. From Fig. In the case of the lat- ter one, a considerable increase in lifespan can be achieved. Therefore, our algorithm the latency is also going to be increased. Performance analysis of the fading-aware routing with energy balancing 6. We duced for reliable energy aware routing in WSNs.
The second packets directly to the BS. The performance of these methods has been the longest lasting node. In each time instant a new packet has tested by extensive simulations which also demonstrated the been generated randomly by one of the nodes being still opera- improvement on the lifespan. The This article is an extended version of previous work published average delay of the different protocols is depicted by Fig. Proof of Theorem 1 is the number of edges in the DAG.
As the reliability of packet transfer is! Therefore, we have the following Gi0 ;i1 ; Gi1 ;i2 ;. Appendix B. Proof of Lemmas 1 and 2 B. Complexity analysis B. This will yield a better solution for thus the path in C. However, if C. Chong, S. Appendix D. Proof of Theorem 4 [2] A. Rogers, D. Corkill, N. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor First we show that Ak is monotone increasing. Let us denote the networks: a survey, Computer Networks 38 — Perkins, P.
Then [6] C. Perkins, E. Zamalloa, B. Then we select a new path 2 1— Zorzi, R. Couto, D. De, C. Daniel, R. Morris, D. Aguayo, J. Bicket, A high- throughput path metric for multi-hop wireless routing, Wireless Networks 11 Y 4 — Impulse response Model of Multipath channel 11 Impulse Response Model of Multipath channel 12 Discrete time Impulse Response Model of Multipath channel 14 It P t has time duration much smaller than the impulse response of multipath channel, the received power delay profile in local area can be Where the gain k relates the power of input pulse to the received power.
At the receiver the signal is amplified and detected using an envelop detector. It is then stored on a high speed digital oscilloscope. If the receiver is set on averaging mode, the local average power delay profile is obtained 19 At receiver signal is despread using same PN The transmitter chip clock rate is a little faster then the receiver chip clock rate The result is sliding correlator. If the sequences are not maximally correlated then the mixer will further despread the signal 22 Transmitter receiver synchronization is eliminated using sliding correlator.
Disadvantages: Measurement are not made real time The associated time required is more Phase information is lost. The S-parameter test set is used to monitor the frequency response of the channel. The frequency sweeper scans a particular frequency band by stepping through the discrete frequencies.
Frequency Domain Channel Sounding 27 It is also called excess delay spread. RMS Delay Spread 33 It specifies the frequency range over which a channel affects the signal spectrum nearly in the same way, causing an approximately constant attenuation and linear change in phase The rms delay spread and coherence bandwidth are inversely proportional to each other. Two sinusoids with frequency separation greater than Bc are affected quite differently by the channel.
If we define Coherence Bandwidth BC as the range of frequencies over which the frequency correlation is above 0. Doppler Spread and Coherence time are parameters which describe the time varying nature of the channel in a small-scale region. Time varying nature of channel caused either by relative motion between BS and mobile or by motions of objects in channel are categorized by BD and Tc 39 The channel has a flat transfer function with almost linear phase, thus affecting all spectral components of the signal in the same way May cause deep fades.
Occurs when channel multipath delay spread is greater than the symbol period. The channel changes because of receiver motion. With the received signal we mean the baseband signal, namely the envelope of the received signal i. Its is a statistical characterization of the multipath fading. Describes the received signal envelope distribution for channels where one of the multipath components is LOS component.
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