A Scalable VLSI Architecture for Soft-Input Soft-Output Depth-First Sphere Decoding

Multiple-input multiple-output (MIMO) wireless transmission imposes huge challenges on the design of efficient hardware architectures for iterative receivers. A major challenge is soft-input soft-output (SISO) MIMO demapping, often approached by sphere decoding (SD). In this paper, we introduce the - to our best knowledge - first VLSI architecture for SISO SD applying a single tree-search approach. Compared with a soft-output-only base architecture similar to the one proposed by Studer et al. in IEEE J-SAC 2008, the architectural modifications for soft input still allow a one-node-per-cycle execution. For a 4x4 16-QAM system, the area increases by 57% and the operating frequency degrades by 34% only.Comment: Accepted for IEEE Transactions on Circuits and Systems II Express Briefs, May 2010. This draft from April 2010 will not be updated any more. Please refer to IEEE Xplore for the final version. *) The final publication will appear with the modified title "A Scalable VLSI Architecture for Soft-Input Soft-Output Single Tree-Search Sphere Decoding

Reduced Complexity Sphere Decoding

In Multiple-Input Multiple-Output (MIMO) systems, Sphere Decoding (SD) can achieve performance equivalent to full search Maximum Likelihood (ML) decoding, with reduced complexity. Several researchers reported techniques that reduce the complexity of SD further. In this paper, a new technique is introduced which decreases the computational complexity of SD substantially, without sacrificing performance. The reduction is accomplished by deconstructing the decoding metric to decrease the number of computations and exploiting the structure of a lattice representation. Furthermore, an application of SD, employing a proposed smart implementation with very low computational complexity is introduced. This application calculates the soft bit metrics of a bit-interleaved convolutional-coded MIMO system in an efficient manner. Based on the reduced complexity SD, the proposed smart implementation employs the initial radius acquired by Zero-Forcing Decision Feedback Equalization (ZF-DFE) which ensures no empty spheres. Other than that, a technique of a particular data structure is also incorporated to efficiently reduce the number of executions carried out by SD. Simulation results show that these approaches achieve substantial gains in terms of the computational complexity for both uncoded and coded MIMO systems.Comment: accepted to Journal. arXiv admin note: substantial text overlap with arXiv:1009.351

Successive interference cancellation aided sphere decoder for multi-input multi-output systems

In this paper, sphere decoding algorithms are proposed for both hard detection and soft processing in multi-input multi-output (MIMO) systems. Both algorithms are based on the complex tree structure to reduce the complexity of searching the unique minimum Euclidean distance and multiple Euclidean distances, and obtain the corresponding transmit symbol vectors. The novel complex hard sphere decoder for MIMO detection is presented first, and then the soft processing of a novel sphere decoding algorithm for list generation is discussed. The performance and complexity of the proposed techniques are demonstrated via simulations in terms of bit error rate (BER), the number of nodes accessed and floating-point operations (FLOPS)

ROBUST ITERATIVEPRUNED-TREE DETECTION ANDLDPCC DECODING

A novel sub-optimal low-complexity equalization and turbo-iterative decoding scheme based on running the sum-product algorithm on an aggressively pruned tree is proposed in this paper for use in a multiple transmit and receive antenna (MIMO) system operating over severe frequency-selective fading inter-symbol interference (ISI) channels. The receiver deals with the issue of signal processing complexity which with a full-search equalization grows with power-law. The sum-product algorithm is applied to the pruned tree which is constructed by two main operations, a sphere list detection and a threshold-based tree search algorithms. At a particular node of the tree, only a number of most probable branches in the tree of hypothetical symbols are expanded and included in the list of candidates; at a particular tree-section, all but some of most probable candidatesare pruned. This pruned tree takes the soft input and generates the soft output, and is utilized in the turbo-iterative manner with the decoder of the low-density parity check code. We oobtained the approximated error probability using the pair-wise error calculation averaged over the fading ensemble, and use it to boundour simulation results. Our current simulation results are obtained for MIMO systems up to four transmit and four receive antennas, using 4-QAM symbols. They indicate the proposed receiverperforms extremely well. The proposed transceiver system is ideal for a system of higher spectral efficiency with even larger signal constellations. Adopting Hassbi-Vikalo's framework, we provide a method which enables a quick evaluation of the signal processing complexity required in the proposed algorithm at a given set of system parameters

Low-complexity dominance-based Sphere Decoder for MIMO Systems

The sphere decoder (SD) is an attractive low-complexity alternative to maximum likelihood (ML) detection in a variety of communication systems. It is also employed in multiple-input multiple-output (MIMO) systems where the computational complexity of the optimum detector grows exponentially with the number of transmit antennas. We propose an enhanced version of the SD based on an additional cost function derived from conditions on worst case interference, that we call dominance conditions. The proposed detector, the king sphere decoder (KSD), has a computational complexity that results to be not larger than the complexity of the sphere decoder and numerical simulations show that the complexity reduction is usually quite significant

Circular Sphere Decoding: A Low Complexity Detection for MIMO Systems with General Two-dimensional Signal Constellations

We propose a low complexity complex valued Sphere Decoding (CV-SD) algorithm, referred to as Circular Sphere Decoding (CSD) which is applicable to multiple-input multiple-output (MIMO) systems with arbitrary two dimensional (2D) constellations. CSD provides a new constraint test. This constraint test is carefully designed so that the element-wise dependency is removed in the metric computation for the test. As a result, the constraint test becomes simple to perform without restriction on its constellation structure. By additionally employing this simple test as a prescreening test, CSD reduces the complexity of the CV-SD search. We show that the complexity reduction is significant while its maximum-likelihood (ML) performance is not compromised. We also provide a powerful tool to estimate the pruning capacity of any particular search tree. Using this tool, we propose the Predict-And-Change strategy which leads to a further complexity reduction in CSD. Extension of the proposed methods to soft output SD is also presented.Comment: Published in IEEE Trans. Vehicular Technolog