scholarly journals SYMBOL LEVEL DECODING FOR DUO-BINARY TURBO CODES

2017 ◽  
Vol 18 (1) ◽  
pp. 111-131 ◽  
Author(s):  
Yogesh Beeharry ◽  
Tulsi Pawan Fowdur ◽  
Krishnaraj M. S. Soyjaudah
Keyword(s):  
Computational Complexity ◽  
Turbo Codes ◽  
Complexity Analysis ◽  
Error Floor ◽  
Decoding Complexity ◽  
Decoding Algorithms ◽  
Level Information ◽  
Simulation Results

This paper investigates the performance of three different symbol level decoding algorithms for Duo-Binary Turbo codes. Explicit details of the computations involved in the three decoding techniques, and a computational complexity analysis are given. Simulation results with different couple lengths, code-rates, and QPSK modulation reveal that the symbol level decoding with bit-level information outperforms the symbol level decoding by 0.1 dB on average in the error floor region. Moreover, a complexity analysis reveals that symbol level decoding with bit-level information reduces the decoding complexity by 19.6 % in terms of the total number of computations required for each half-iteration as compared to symbol level decoding.

A Min-Sum Algorithm Suitable for Hardware Implementation Based on LDPC Codes

2011 ◽  
Vol 271-273 ◽  
pp. 458-463
Author(s):  
Rui Ping Chen ◽  
Zhong Xun Wang ◽  
Xin Qiao Yu
Keyword(s):  
Hardware Implementation ◽  
Ldpc Codes ◽  
Low Complexity ◽  
Scaling Factor ◽  
Computation Complexity ◽  
Decoding Complexity ◽  
Decoding Algorithms ◽  
Hardware Resource ◽  
Simulation Results ◽  
Ber Performance

Decoding algorithms with strong practical value not only have good decoding performance, but also have the computation complexity as low as possible. For this purpose, the paper points out the modified min-sum decoding algorithm(M-MSA). On the condition of no increasing in the decoding complexity, it makes the error-correcting performance improved by adding the appropriate scaling factor based on the min-sum algorithm(MSA), and it is very suitable for hardware implementation. Simulation results show that this algorithm has good BER performance, low complexity and low hardware resource utilization, and it would be well applied in the future.

Computational Complexity Analysis for a Class of Symmetric Cryptosystems Using Simple Arithmetic Operations and Memory Access Time

2013 ◽  
Vol 7 (1) ◽  
pp. 63-75 ◽  
Author(s):  
Walid Y. Zibideh ◽  
Mustafa M. Matalgah
Keyword(s):  
Computational Complexity ◽  
Computer Simulations ◽  
Complexity Analysis ◽  
Memory Access ◽  
Access Time ◽  
Simple Arithmetic ◽  
Design And Implementation ◽  
Simulation Results ◽  
Encryption Decryption ◽  
Symmetric Block

A secure cryptosystem could be very complicated, time consuming and hard to implement. Therefore, the complexity of the cryptosystem should be taken into account during design and implementation. In this work, the authors introduce a comprehensive and platform independent complexity analysis for a class of symmetric block cryptosystems, by which it will be easier to evaluate the performance of some used cryptosystems. Previous works lacked the comprehensiveness in their analysis, due to the fact that the memory access time was completely ignored, which greatly degrades the accuracy of the analysis and limits it to one data block only. In this paper the authors analytically compute the complexity for a class of symmetric cryptosystems in terms of the number of the clock cycles required and in terms of the required time for encryption/decryption, independently of the hardware or software used in the encryption/decryption process. Moreover, this is the first complexity analysis that considers the required time to access and retrieve information from memory, which makes the analysis more comprehensive and accurate than previous work as well as being general for any number of encryption data blocks. In addition, computer simulations are used to truly evaluate the accuracy of the analysis and to show how the analytical results match the simulation results.

A Unitary-UCA-Root-MUSIC Algorithm Based on MSWF

2014 ◽  
Vol 610 ◽  
pp. 339-344
Author(s):  
Qiang Guo ◽  
Yun Fei An
Keyword(s):  
Computer Simulation ◽  
Computational Complexity ◽  
Real Time ◽  
Unitary Transformation ◽  
Wiener Filter ◽  
Doa Estimation ◽  
Signal Subspace ◽  
Music Algorithm ◽  
Multi Stage ◽  
Simulation Results

A UCA-Root-MUSIC algorithm for direction-of-arrival (DOA) estimation is proposed in this paper which is based on UCA-RB-MUSIC [1]. The method utilizes not only a unitary transformation matrix different from UCA-RB-MUSIC but also the multi-stage Wiener filter (MSWF) to estimate the signal subspace and the number of sources, so that the new method has lower computational complexity and is more conducive to the real-time implementation. The computer simulation results demonstrate the improvement with the proposed method.

scholarly journals Efficient MAP-algorithm implementation on programmable architectures

2003 ◽  
Vol 1 ◽  
pp. 259-263 ◽  
Author(s):  
F. Kienle ◽  
H. Michel ◽  
F. Gilbert ◽  
N. Wehn
Keyword(s):  
Turbo Codes ◽  
Communication Systems ◽  
Building Blocks ◽  
Communication Performance ◽  
Channel Decoding ◽  
Turbo Decoder ◽  
Decoding Algorithms ◽  
Map Algorithm ◽  
Algorithm Implementation ◽  
Log Map

Abstract. Maximum-A-Posteriori (MAP) decoding algorithms are important HW/SW building blocks in advanced communication systems due to their ability to provide soft-output informations which can be efficiently exploited in iterative channel decoding schemes like Turbo-Codes. Multi-standards demand flexible implementations on programmable platforms. In this paper we analyze a quantized turbo-decoder based on a Max-Log-MAP algorithm with Extrinsic Scaling Factor (ESF). Its communication performance approximate to a Turbo-Decoder with a Log-MAP algorithm and is less sensitive to quantization effects. We present Turbo-Decoder implementations on state-of-the-art DSPs and show that only a Max-Log-MAP implementation fulfills a throughput requirement of ~2 Mbit/s. The negligible overhead for the ESF implementation strengthen the use of Max-Log-MAP with ESF implementation on programmable platforms.

Complexity Issues within Eigenvalue-Based Multi-Antenna Spectrum Sensing

2015 ◽  
pp. 603-617 ◽  
Author(s):  
Ines Elleuch ◽  
Fatma Abdelkefi ◽  
Mohamed Siala
Keyword(s):  
Computational Complexity ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Complexity Analysis ◽  
Floating Point ◽  
Beam Forming ◽  
Multiple Antenna ◽  
Deep Insight ◽  
Minimum Eigenvalue ◽  
Insight Into

This chapter provides a deep insight into multiple antenna eigenvalue-based spectrum sensing algorithms from a complexity perspective. A review of eigenvalue-based spectrum-sensing algorithms is provided. The chapter presents a finite computational complexity analysis in terms of Floating Point Operations (flop) and a comparison of the Maximum-to-Minimum Eigenvalue (MME) detector and a simplified variant of the Multiple Beam forming detector as well as the Approximated MME method. Constant False Alarm Performances (CFAR) are presented to emphasize the complexity-reliability tradeoff within the spectrum-sensing problem, given the strong requirements on the sensing duration and the detection performance.

Multistage Spectrum Sensing for Cognitive Radio Using Energy and Maximum Eigenvalues Detection

2020 ◽  
Vol 11 (4) ◽  
pp. 41-59
Author(s):  
Faten Mashta ◽  
Mohieddin Wainakh ◽  
Wissam Altabban
Keyword(s):  
Cognitive Radio ◽  
Computational Complexity ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Smoothing Factor ◽  
Third Stage ◽  
Simulation Results ◽  
Good Detection ◽  
High Computational Complexity

Spectrum sensing in cognitive radio has difficult and complex requirements such as requiring speed and sensing accuracy at very low SNRs. In this paper, the authors propose a novel fully blind sequential multistage spectrum sensing detector to overcome the limitations of single stage detector and make use of the advantages of each detector in each stage. In first stage, energy detection is used because of its simplicity. However, its performance decreases at low SNRs. In second and third stage, the maximum eigenvalues detector is adopted with different smoothing factor in each stage. Maximum eigenvalues detection technique provide good detection performance at low SNRs, but it requires a high computational complexity. In this technique, the probability of detection improves as the smoothing factor raises at the expense of increasing the computational complexity. The simulation results illustrate that the proposed detector has better sensing accuracy than the three individual detectors and a computational complexity lies in between the three individual complexities.

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