Parallelized architectures for low latency turbo structures

Gazi, Orhan
In this thesis, we present low latency general concatenated code structures suitable for parallel processing. We propose parallel decodable serially concatenated codes (PDSCCs) which is a general structure to construct many variants of serially concatenated codes. Using this most general structure we derive parallel decodable serially concatenated convolutional codes (PDSCCCs). Convolutional product codes which are instances of PDSCCCs are studied in detail. PDSCCCs have much less decoding latency and show almost the same performance compared to classical serially concatenated convolutional codes. Using the same idea, we propose parallel decodable turbo codes (PDTCs) which represent a general structure to construct parallel concatenated codes. PDTCs have much less latency compared to classical turbo codes and they both achieve similar performance. We extend the approach proposed for the construction of parallel decodable concatenated codes to trellis coded modulation, turbo channel equalization, and space time trellis codes and show that low latency systems can be constructed using the same idea. Parallel decoding operation introduces new problems in implementation. One such problem is memory collision which occurs when multiple decoder units attempt accessing the same memory device. We propose novel interleaver structures which prevent the memory collision problem while achieving performance close to other interleavers.


Decentralized estimation under communication constraints
Üney, Murat; Leblebicioğlu, Mehmet Kemal; Department of Electrical and Electronics Engineering (2009)
In this thesis, we consider the problem of decentralized estimation under communication constraints in the context of Collaborative Signal and Information Processing. Motivated by sensor network applications, a high volume of data collected at distinct locations and possibly in diverse modalities together with the spatially distributed nature and the resource limitations of the underlying system are of concern. Designing processing schemes which match the constraints imposed by the system while providing a ...
Improvements in DOA estimation by array interpolation in non-uniform linear arrays
Yaşar, Temel Kaya; Tuncer, Temel Engin; Department of Electrical and Electronics Engineering (2006)
In this thesis a new approach is proposed for non-uniform linear arrays (NLA) which employs conventional subspace methods to improve the direction of arrival (DOA) estimation performance. Uniform linear arrays (ULA) are composed of evenly spaced sensor elements located on a straight line. ULA's covariance matrix have a Vandermonde matrix structure, which is required by fast subspace DOA estimation algorithms. NLA differ from ULA only by some missing sensor elements. These missing elements cause some gaps in...
Frequency invariant beamforming and its application to wideband direction of arrival estimation
Babataş, Eren; Candan, Çağatay; Department of Electrical and Electronics Engineering (2008)
In this thesis the direction of arrival estimation of wideband signals using frequency invariant beamforming method is examined. The difficulty with the direction of arrival estimation of wideband signals is that it is not possible to obtain a single covariance matrix valid for the whole frequency spectrum of the signal. There are various methods proposed in the literature to overcome this difficulty. The common aim of all the methods is to obtain a composite covariance matrix for the overall band of the si...
Dense depth map estimation for object segmentation in multi-view video
Çığla, Cevahir; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2007)
In this thesis, novel approaches for dense depth field estimation and object segmentation from mono, stereo and multiple views are presented. In the first stage, a novel graph-theoretic color segmentation algorithm is proposed, in which the popular Normalized Cuts 59H[6] segmentation algorithm is improved with some modifications on its graph structure. Segmentation is obtained by the recursive partitioning of the weighted graph. The simulation results for the comparison of the proposed segmentation scheme w...
Improvements to neural network based restoration in optical networks
Türk, Fethi; Bilgen, Semih; Department of Electrical and Electronics Engineering (2008)
Performance of neural network based restoration of optical networks is evaluated and a few possible improvements are proposed. Neural network based restoration is simulated with optical link capacities assigned by a new method. Two new improvement methods are developed to reduce the neural network size and the restoration time of severed optical connections. Cycle based restoration is suggested, which reduces the neural network structure by restoring the severed connections for each optical node, iterativel...
Citation Formats
O. Gazi, “Parallelized architectures for low latency turbo structures,” Ph.D. - Doctoral Program, Middle East Technical University, 2007.