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Even though it s not necessarily fashionable to admit it, I like SCSI even though it s not the newest kid on the block, and here s why:

where INr is the Nr Nr identity matrix, is the mean SNR per RX branch, and Rss is the correlation matrix of the transmit data (if data at the different antenna elements are uncorrelated, it is a diagonal matrix with entries that describe the power distribution among antennas).12 The distribution of power among the different eigenmodes (or antennas) depends on the amount of CSIT; we also assume for the moment that the RX has perfect CSI. The equations above con rm our intuitive picture that capacity increases linearly with min(Nt , Nr , Ns ), as the number of nonzero singular values RH is upper-limited by min(Nt , Nr , Ns ). No Channel State Information at the Transmitter and Full CSI at the Receiver When the RX knows the channel perfectly, but no CSI is available at the TX, it is optimum to assign equal transmit power to all TX antennas, Pk = P /Nt , and use uncorrelated data streams. Capacity thus takes on the form: C = log2 det INr + HH Nt (20.38)

d in reste I n te s M ai l gi ng ou cha n ce Y an pear ap so m e : Fo r a re ca n t e re , th easo n io n r ra t n f igu s n o co r t hi n s fo o p t io re . fea tu

Combination (direction and magnitude)

When you create a task, you should always try to run it before relying on Task Scheduler to do the work for you, in order to ensure that it is working correctly. If it doesn t run, check the following:

12 demonstrated that Bit Error Rates (BERs) on the order of 10 2 can occur for Signalto-Noise Ratios (SNRs) typically encountered in wireless systems. Those high BERs are mostly due to the effect of multipath propagation. Advanced receiver structures can help to reduce those values: diversity combats fading dips, while equalizers and Rake receivers (see s 16 and 18) improve the performance in frequency-selective channels. However, even those advanced receivers might not suf ciently reduce the BER. Data communications often require BERs on the order of 10 6 10 9 . Such low values can only be achieved by employing coding of the data, i.e., introducing redundancy into the transmission. The use of error-correcting codes1 leads to a reduction of the BER, or equivalently to a coding gain Gcode , i.e., we have to use Gcode decibel (dB) less transmit power to achieve the target BER than in an uncoded system.2 The history of coding starts with the seminal work of Claude Shannon [Shannon 1948] on The mathematical theory of communication. He showed that it is possible to transmit data without errors as long as the bit rate is smaller than the channel capacity. The absence of errors is achieved by the use of appropriate codes. Shannon showed that (in nitely long) random codes achieve capacity. Unfortunately, such codes cannot be used in practice due to the enormous effort required for their decoding. For more than 50 years, the work of the coding theorists mainly consisted of nding practical codes that come close to the Shannon limit, i.e., allow communications with rates close to the channel capacity. In the subsequent sections of this chapter, we will give a brief overview of error-correction coding. The basic coding theory holds for temporally constant and temporally varying channels; Sections 14.2 14.7 thus do not distinguish between those cases. Rather, they lay out the theoretical background of the most important classes of codes and their decoding: block codes, convolutional codes, Trellis Coded Modulation (TCM), turbo codes, and Low Density Parity Check (LDPC) codes. Sections 14.8 14.9 then deals speci cally with the idiosyncrasies of fading channels and describes how the coding structures need to be adapted for this case.

among the members of P. Under quite general conditions, there is one and only one such Fo, as we shall see below. For any sequence (T,) of estimates, the asymptotic variance of f i T , at FOis at best l / I ( F o ) ;see Section 3.5. If we can find a sequence (T,) such that its asymptotic variance does not exceed l / I ( F o )for any F E P, have clearly solved the minimax we problem. In particular, this sequence (T,) must be asymptotically efficient for Fo, which gives a hint where to look for asymptotic minimax estimates.

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Even when encrypted les are backed up to media that doesn t support encryption (such as FAT volumes or oppies), Backup maintains the encryption on the les. If an unauthorized user gains access to the backup set and restores it whether to FAT or NTFS volumes the encrypted data is not restored. Backup creates the target les, but the les are empty.