Adaptation Techniques and Enabling Parameter Estimation Algorithms for Wireless Communications Systems - Adaptive Signal Processing in Wireless Communications - page 7

the same quality of service. In order to satisfy the desired BER performance, instead of
running a computationally complex algorithm for all channel conditions, the receiver
can choose the most appropriate algorithm given the system and channel conditions.
Advanced baseband signal processing techniques play a significant role in receiver
adaptation. Baseband algorithms used for time and frequency synchronization, baseband
filtering, channel estimation and tracking, demodulation and equalization, interference
cancellation, soft information calculation, antenna selection and combining, decoding,
etc., can be made adaptive depending on the channel and interference conditions.
Conventional receiver algorithms are designed for the worst-case channel and inter-
ferer conditions. For example, the channel estimation and tracking algorithms assume
the worst-case mobile speed; the channel equalizers assume the worst-case channel dis-
persion; the interference cancellation algorithms assume that the interferer is always
active and constant; and so on. Adaptive receiver design measures the current channel
and interferer conditions and tunes the specific receiver function that is most appropri-
ate for the current conditions. For example, a specific demodulation technique may work
well in some channel conditions, but might not provide good performance in others.
Hence, a receiver might include a variety of demodulators that are individually tuned to
a set of channel classes. If the receiver could demodulate the data reliably with a simpler
and less complex receiver algorithm under the given conditions, then it is desired to use
that algorithm for demodulation.

1.3 Parameter Measurements

Many adaptation techniques require estimation of various quantities like channel selec-
tivity, link quality, network load and congestion, etc. Here, we focus more on physical
layer measurements from a digital signal processing perspective. As discussed earlier,
link quality measures have many applications for various adaptation strategies. In addi-
tion, information on channel selectivity in time, frequency, and space is very useful for
adaptation of wireless communications systems. In this section, these important param-
eters and their estimation techniques will be discussed.

1.3.1 Channel Selectivity Estimation
In wireless communications, the transmitted signal reaches the receiver through a num-
ber of different paths. Multipath propagation causes the signal to be spread in time, fre-
quency, and angle. These spreads, which are related to the selectivity of the channel, have
significant implications on the received signal. A channel is considered to be selective if
it varies as a function of time, frequency, or space. The information on the variation of
the channel in time, frequency, and space is very crucial in adaptation of wireless com-
munications systems.

1.3.1.1 Time Selectivity Measure: Doppler Spread
Doppler shift is the frequency shift experienced by the radio signal when either the
transmitter or receiver is in motion, and Doppler spread is a measure of the spectral