318
M. Z. Saleh et al.
The need for better compression increases with the increasing amount of data
transfer nowadays. Huffman coding (HC) introduced by David A. Huffman in 1952,
and arithmetic coding (AC) are the two most known coding algorithms. Although
HC is easy, it usually does not compress as well as AC [9]. Arithmetic coding,
however, requires more computing power, which is not always present in low power
embedded systems [9]. Researcher Jarek Duda [10], presented a unique method
to entropy coding. A family of generalizations of standard numeral systems that
are suitable for encoding equiprobable symbol sequences into asymmetric numeral
systems (ANS) to be optimal for freely selected symbol probability distributions. It
has some similarities to arithmetic coding, but in selecting a range, it extends these
ranges evenly over the entire interval instead of encoding symbols. This technique
is much simpler where instead of using two states to define the range, this method
needs only one state to define the range. There are practical findings for the binary
case, the asymmetric binary system (ABS) [11]. Formulas that provide a highly
accurate entropy encoder for which the probability of symbol distribution will freely
change. However, for the general case, instead of using formulas ANS initially uses
the pseudorandom number generator to distribute symbols with supposed statistics.
The accuracy can still be very high, but the drawback is that it needs to be reinitialized
as the probability distribution changes. The benefit is that a few bits in one use of the
table will get compression rates like in AC and transfers like in HC during encoding
and decoding [11].
30.3 Results and Discussion
There were several measuring methods to validate the simulation results for PAPR
improvements that are mainly solved by using the EAAC technique that is mentioned
in this study. This can be classified into two sections such as follows:
a. Bit Error Rate (BER) Measurements
Referring to Fig. 30.2, the system observed to have AAC at SNR 9.9 dB and
proved to have lower BER as compared to AC and HC as verification elements.
This indicated that the new AAC reduced the probability of error of the system
when input and output are said to be compared (Tables 30.1 and Table 30.2).
Meanwhile, Fig. 30.3 shows the elements of BER at the clip rate 10
–1 which
gives the value of 8.55 signal-to-noise ratio with 100 number of users and made
comparison with input and output system model.
The analysis stated that, with STFBC MIMO F-OFDM, the study proved to have
new AAC to reduce BER in the system.
b. PAPR Measurements
Figure 30.4 shows the PAPR measurement at the transmitter with 8.5 dB using AC
as compared to HC for verification elements. In specific, the system has improved
the reliability of signal transmitting as compared to HC. A further requirement
M. Z. Saleh et al.
The need for better compression increases with the increasing amount of data
transfer nowadays. Huffman coding (HC) introduced by David A. Huffman in 1952,
and arithmetic coding (AC) are the two most known coding algorithms. Although
HC is easy, it usually does not compress as well as AC [9]. Arithmetic coding,
however, requires more computing power, which is not always present in low power
embedded systems [9]. Researcher Jarek Duda [10], presented a unique method
to entropy coding. A family of generalizations of standard numeral systems that
are suitable for encoding equiprobable symbol sequences into asymmetric numeral
systems (ANS) to be optimal for freely selected symbol probability distributions. It
has some similarities to arithmetic coding, but in selecting a range, it extends these
ranges evenly over the entire interval instead of encoding symbols. This technique
is much simpler where instead of using two states to define the range, this method
needs only one state to define the range. There are practical findings for the binary
case, the asymmetric binary system (ABS) [11]. Formulas that provide a highly
accurate entropy encoder for which the probability of symbol distribution will freely
change. However, for the general case, instead of using formulas ANS initially uses
the pseudorandom number generator to distribute symbols with supposed statistics.
The accuracy can still be very high, but the drawback is that it needs to be reinitialized
as the probability distribution changes. The benefit is that a few bits in one use of the
table will get compression rates like in AC and transfers like in HC during encoding
and decoding [11].
30.3 Results and Discussion
There were several measuring methods to validate the simulation results for PAPR
improvements that are mainly solved by using the EAAC technique that is mentioned
in this study. This can be classified into two sections such as follows:
a. Bit Error Rate (BER) Measurements
Referring to Fig. 30.2, the system observed to have AAC at SNR 9.9 dB and
proved to have lower BER as compared to AC and HC as verification elements.
This indicated that the new AAC reduced the probability of error of the system
when input and output are said to be compared (Tables 30.1 and Table 30.2).
Meanwhile, Fig. 30.3 shows the elements of BER at the clip rate 10
–1 which
gives the value of 8.55 signal-to-noise ratio with 100 number of users and made
comparison with input and output system model.
The analysis stated that, with STFBC MIMO F-OFDM, the study proved to have
new AAC to reduce BER in the system.
b. PAPR Measurements
Figure 30.4 shows the PAPR measurement at the transmitter with 8.5 dB using AC
as compared to HC for verification elements. In specific, the system has improved
the reliability of signal transmitting as compared to HC. A further requirement