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This type of coupling is necessary to ensure ef cient allocation of the precious wireless resource because transmissions during times of low channel quality tend to fail and hence lead to a waste of the channel resource, leading to lower network throughput Vertical coupling can be performed in the following ways: 1 Control the physical layer parameters, such as transmission power, modulation scheme, and coding level with respect to the channel quality at the scheduled transmission time 2 Control the scheduled time to avoid transmission during low channel quality One way to control the transmission parameters at the physical layer depends on the time-varying channel quality For example, at the time of deciding which user to transmit, a scheduling algorithm necessitate adjusting the modulation level and transmission power of the user appropriately with respect to its instantaneous channel quality [3 5].



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Accurate synchronization is extremely important for UWB communication systems due to extremely short duration pulses employed. The reasons for timing errors in UWB systems include timing jitter (which can be due to transceiver clock instabilities), and nger estimation error. While the timing jitter is typically on the order of 10 ps with the current transistor technology and is usually modeled by a Gaussian or uniform distribution [42, 43], distribution of nger estimation error depends on the pulse shape and the noise power [42], but may as well be modeled via uniform distribution for analytical tractability [44]. Timing mismatches between the correlator template and the received signal can result in serious degradations in the BER. Given a xed amount of mismatch ej between the correlator template and the received signal, the SNR degradation is a function of the normalized pulse correlation function, and will be different for different data mapping formats. The BER performances of four different modulation formats for a xed timing misalignment are presented in Table 4.4, and are depicted in Figure 4.16 for SNR 10 dB. It is observed that the degradations in BPSK and PPM are similar, while OOK performs worse for large jitter. BER equation in





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CASE 1 Cyc (short for encyclopedia) is an AI (artificial intelligence) application developed by Cycorp Inc. in Austin, Texas. The system is the brainchild of Douglas B. Lenat. Cyc uses a topdown system to organize knowledge within its knowledge base and to enable the storage of up to 2 million rules. A half-dozen companies including Glaxo Wellcome, Digital Equipment, IBM, and United Healthcare have snapped up the first commercial versions. The ability of Cyc to store mass quantities of data and rules helps United Healthcare manage huge online thesauruses of pharmaceutical and health care terms that have associations based on drug contents and/or interaction effects, thus limiting the need for employees to deal with the complexity of terms. CASE 2 A newer product based on Cyc is a photo retrieval system. The knowledge base in Cyc is able to learn the relationships between different phrases and use these relationships to search related photographs stored in files along with searchable captions. Type strong and daring person and Cyc pulls up a picture captioned man climbing mountain. Cyc knows that a man is a person, and that mountain climbing demands strength and is dangerous. CASE 3 Firefly is a web-based Cyc derivative that allows viewers to identify music and artists they like. Based on the information that thousands of other users have entered, Firefly

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