Area and Energy Efficient QCA Based Compact Serial Concatenated Convolutional Code Encoder

Quantum-dot Cellular Automata (QCA) is a transistor-less technology known for its low power consumption and higher clock rate. Serial Concatenated Convolutional Coding (SCCC) encoder is a class of forward error correction. This paper picturizes the implementation of the outer encoder as a (7, 4, 1) Bose Chaudhary Hocquenghem encoder that serves the purpose of burst error correction, a pseudo-random inter-leaver used for permuting of systematic code words and finally the inner encoder which is used for the correction of random errors in QCA. Two different architectures of the SCCC encoder have been proposed and discussed in this study. In the proposed two architectures, the first based on external clock signals whereas the second based on internal clock generation. The sub-blocks outer encoder, pseudo-random inter-leaver and inner encoder of the SCCC encoder are optimized, implemented and simulated using QCADesigner and then integrated to design a compact SCCC encoder. The energy dissipation is computed using QCADesigner-E. The proposed SCCC encoder reduced the total area by 46% and energy dissipation by 50% when compared to the reference SCCC encoder. The proposed encoders are more efficient in terms of cell count, energy dissipation and area occupancy respectively.

Analisis Kinerja FBMC OQAM menggunakan Kode Konvolusi

ABSTRAKSistem FBMC merupakan teknologi MCM yang dapat menyediakan laju data bit yang tinggi. Modulasi digital OQAM digunakan untuk meningkatkan bit rate. Pengkodean kanal digunakan untuk mengoreksi kesalahan yang diakibatkan noise. Penilitian ini menggunakan pengkodean kanal kode konvolusi yang digunakan pada bagian pengirim dan algortima viterbi pada bagian penerima. Simulasi dilakukan pada FBMC OQAM dengan kode konvolusi dan tanpa kode konvolusi dengan perbandingan parameter BER dan kapasitas kanal terhadap SNR. Hasil penelitian menunjukan FBMC OQAM dengan kode konvolusi lebih baik daripada FBMC OQAM tanpa kode konvolusi pada SNR tinggi. Pada FBMC OQAM untuk mencapai BER 10-3 membutuhkan SNR 17 dB sedangkan pada FBMC OQAM dengan kode konvolusi membutuhkan SNR 16 dB. Peningkatan SNR dapat meningkatkan kapasitas kanal yang dihasilkan, pada SNR 0 dB menghasilkan 0,4535 bps/Hz dan SNR 20 dB menghasilkan 5,858 bps/Hz.Kata kunci: kode konvolusi, algoritma viterbi, FBMC, OQAM, BER ABSTRACTThe FBMC system is an MCM technology that can provide high bit data rates. OQAM digital modulation is used to increase the bit rate. Channel coding is used to correct errors caused by noise. This research uses convolutional code channel coding used on the sender and viterbi algorithms on the receiver. Simulations are carried out on FBMC OQAM with convolutional code and without convolutional code with a comparison of BER parameters and channel capacity to SNR. The results showed that FBMC OQAM with convolutional code was better than FBMC OQAM without convolutional code at high SNR. In FBMC OQAM to reach BER 10-3 requires SNR of 17 dB while in FBMC OQAM with convolutional code requires SNR of 16 dB. Increasing SNR can increase the resulting channel capacity, at 0 dB SNR it produces 0.4535 bps / Hz and SNR 20 dB produces 5.858 bps / Hz.Keywords: convolutional code, viterbi algorithm, FBMC, OQAM, BER

Construction of LDPC convolutional codes via difference triangle sets

In this paper, a construction of $$(n,k,\delta )$$ ( n , k , δ ) LDPC convolutional codes over arbitrary finite fields, which generalizes the work of Robinson and Bernstein and the later work of Tong is provided. The sets of integers forming a (k, w)-(weak) difference triangle set are used as supports of some columns of the sliding parity-check matrix of an $$(n,k,\delta )$$ ( n , k , δ ) convolutional code, where $$n\in {\mathbb {N}}$$ n ∈ N , $$n>k$$ n > k . The parameters of the convolutional code are related to the parameters of the underlying difference triangle set. In particular, a relation between the free distance of the code and w is established as well as a relation between the degree of the code and the scope of the difference triangle set. Moreover, we show that some conditions on the weak difference triangle set ensure that the Tanner graph associated to the sliding parity-check matrix of the convolutional code is free from $$2\ell $$ 2 ℓ -cycles not satisfying the full rank condition over any finite field. Finally, we relax these conditions and provide a lower bound on the field size, depending on the parity of $$\ell $$ ℓ , that is sufficient to still avoid $$2\ell $$ 2 ℓ -cycles. This is important for improving the performance of a code and avoiding the presence of low-weight codewords and absorbing sets.

An Embedding Strategy Using Q-Ary Convolutional Codes for Large and Small Payloads

Matrix embedding (ME) code is a commonly used steganography technique, which uses linear block codes to improve embedding efficiency. However, its main disadvantage is the inability to perform maximum likelihood decoding due to the high complexity of decoding large ME codes. As such, it is difficult to improve the embedding efficiency. The proposed q-ary embedding code can provide excellent embedding efficiency and is suitable for various embedding rates (large and small payloads). This article discusses that by using perforation technology, a convolutional code with a high embedding rate can be easily converted into a convolutional code with a low embedding rate. By keeping the embedding rate of the (2, 1) convolutional code unchanged, convolutional codes with different embedding rates can be designed through puncturing.

Erasure decoding of convolutional codes using first-order representations

It is well known that there is a correspondence between convolutional codes and discrete-time linear systems over finite fields. In this paper, we employ the linear systems representation of a convolutional code to develop a decoding algorithm for convolutional codes over the erasure channel. In this kind of channel, which is important due to its use for data transmission over the Internet, the receiver knows if a received symbol is correct. We study the decoding problem using the state space description of a convolutional code, and this provides in a natural way additional information. With respect to previously known decoding algorithms, our new algorithm has the advantage that it is able to reduce the decoding delay as well as the computational effort in the erasure recovery process. We describe which properties a convolutional code should have in order to obtain a good decoding performance and illustrate it with an example.

Source-Channel Codec for a WCDMA Based Multimedia System

The project deals with the operation of a Source-Channel Codec for a WCDMA Based Multimedia System. The system is meant to transfer and receive both digitized speech and still image signals. It uses a part of the WCDMA technology to mix up the transmitted signals throughout the implementation of Direct Sequence Spread Spectrum and Chip Sequencing methodologies. The Walsh code algorithm is used to ensure the orthogonality among different Chip Sequences. On the transmitter side the system first offers the formatting stage where both a speech and a still image signal are digitized. The following stage in the system exhibits a significant degree of data compression applying appropriate compression algorithms: Lempel-Ziv-Welch for the speech signal and Huffman Code Algorithm for the still image. These compression algorithms are implemented in the Source Encoder stage of the system. The system also provides basic FEC (Forward Error Correction) capabilities, using both Linear Block Code and Convolutional Code algorithms introduced in the Channel Encoder stage. The goal of these FEC algorithms is to detect and correct errors during the transmission of data due to the channel imperfections. At the WCMDA stage the two signals are added together forming an aggregated signal that is being transmitted through the channel. On the receiver side a digital demodulator separates the aggregated signal into two signals using the feature of the orthogonality of vectors. Then the Channel Decoder stage follows, where both signals, which have gotten corrupted during the transmission through the channel due to channel imperfections, are recovered. The imperfections in the channel are simulated by random noise that is added to the aggregated signal in the WCDMA stage of the system. The last stage in the system, the Source Decoder stage, deals with the conversion of the received signals from the digital to analog form and reconstruction of the signals in the sense that they can be heard (speech) and seen (still image). Each stage in the system is simulated using MATLAB programming language. The report is formed of three major parts; the theoretical part where the theory behind each stage in the system is explained, the example part where applicable numerical examples are provided and analyzed for better understanding of both the theory and the Matlab code, and the result part where the Matlab results for each stage are analayzed.

Source-Channel Codec for a WCDMA Based Multimedia System

The project deals with the operation of a Source-Channel Codec for a WCDMA Based Multimedia System. The system is meant to transfer and receive both digitized speech and still image signals. It uses a part of the WCDMA technology to mix up the transmitted signals throughout the implementation of Direct Sequence Spread Spectrum and Chip Sequencing methodologies. The Walsh code algorithm is used to ensure the orthogonality among different Chip Sequences. On the transmitter side the system first offers the formatting stage where both a speech and a still image signal are digitized. The following stage in the system exhibits a significant degree of data compression applying appropriate compression algorithms: Lempel-Ziv-Welch for the speech signal and Huffman Code Algorithm for the still image. These compression algorithms are implemented in the Source Encoder stage of the system. The system also provides basic FEC (Forward Error Correction) capabilities, using both Linear Block Code and Convolutional Code algorithms introduced in the Channel Encoder stage. The goal of these FEC algorithms is to detect and correct errors during the transmission of data due to the channel imperfections. At the WCMDA stage the two signals are added together forming an aggregated signal that is being transmitted through the channel. On the receiver side a digital demodulator separates the aggregated signal into two signals using the feature of the orthogonality of vectors. Then the Channel Decoder stage follows, where both signals, which have gotten corrupted during the transmission through the channel due to channel imperfections, are recovered. The imperfections in the channel are simulated by random noise that is added to the aggregated signal in the WCDMA stage of the system. The last stage in the system, the Source Decoder stage, deals with the conversion of the received signals from the digital to analog form and reconstruction of the signals in the sense that they can be heard (speech) and seen (still image). Each stage in the system is simulated using MATLAB programming language. The report is formed of three major parts; the theoretical part where the theory behind each stage in the system is explained, the example part where applicable numerical examples are provided and analyzed for better understanding of both the theory and the Matlab code, and the result part where the Matlab results for each stage are analayzed.

Performance comparison of channel coding schemes for 5G massive machine type communications

<p><span>Channel coding for the fifth generation (5G) mobile communication is currently facing new challenges as it needs to uphold diverse emerging applications and scenarios. Massive machine-type communication (mMTC) constitute one of the main usage scenarios in 5G systems, which promise to provide low data rate services to a large number of low power and low complexity devices. Research on efficient coding schemes for such use case is still ongoing and no decision has been made yet. Therefore, This paper compares the performance of different coding schemes, namely: tail-biting convolutional code (TBCC), low density parity check codes (LDPC), Turbo code and Polar codes, in order to select the appropriate channel coding technique for 5G-mMTC scenario. The considered codes are evaluated in terms of bit error rate (BER) and block error rate (BLER) for short information block lengths (K ≤ 256). We further investigate their Algorithmic complexity in terms of the number of basic operations. The Simulation results indicate that polar code with CRC-aided successive cancelation list decoder has better performance compared with other coding schemes for 5G-mMTC scenario.</span></p>