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

Statistical Pruning for Near-Maximum Likelihood Decoding

In many communications problems, maximum-likelihood (ML) decoding reduces to finding the closest (skewed) lattice point in N-dimensions to a given point xisin CN. In its full generality, this problem is known to be NP-complete. Recently, the expected complexity of the sphere decoder, a particular algorithm that solves the ML problem exactly, has been computed. An asymptotic analysis of this complexity has also been done where it is shown that the required computations grow exponentially in N for any fixed SNR. At the same time, numerical computations of the expected complexity show that there are certain ranges of rates, SNRs and dimensions N for which the expected computation (counted as the number of scalar multiplications) involves no more than N3 computations. However, when the dimension of the problem grows too large, the required computations become prohibitively large, as expected from the asymptotic exponential complexity. In this paper, we propose an algorithm that, for large N, offers substantial computational savings over the sphere decoder, while maintaining performance arbitrarily close to ML. We statistically prune the search space to a subset that, with high probability, contains the optimal solution, thereby reducing the complexity of the search. Bounds on the error performance of the new method are proposed. The complexity of the new algorithm is analyzed through an upper bound. The asymptotic behavior of the upper bound for large N is also analyzed which shows that the upper bound is also exponential but much lower than the sphere decoder. Simulation results show that the algorithm is much more efficient than the original sphere decoder for smaller dimensions as well, and does not sacrifice much in terms of performance

A simplified sphere decoding algorithm for MIMO transmission system

In sphere decoding the choice of sphere radius is crucial to excellent performance. In Chan-Lee sphere decoding -based algorithm, the problem of choosing initial radius has been solved by making the radius sufficiently large, thus increasing the size of the search region. In this paper we present maximum likelihood decoding using simplified sphere decoder as apposed to the original sphere decoder for the detection of cubic structure quadrature amplitude modulation symbols. This simple algorithm based on Chan-Lee sphere decoder allows the search for closest lattice point in a reduced complexity manner compared to original sphere decoder for multiple input multiple output system with perfect channel state information at the receiver. Results show symbol error rate has stabilized even at very low initial value of the square radius

Reduced-Complexity Maximum-Likelihood Detection in Multiple-Antenna-Aided Multicarrier Systems

In this contribution we explore a novel Optimized Hierarchy Reduced Search Algorithm (OHRSA)-aided space-time processing method, which may be regarded as an advanced extension of the Complex Sphere Decoder (CSD) method. The algorithm proposed extends the potential application range of the CSD method, as well as reduces the associated computational complexity

Fast sphere decoder for MIMO systems

This work focuses on variants of the conventional sphere decoding technique for Multi Input Multi Output (MIMO) systems. Space Time Block Codes (STBC) have emerged as a popular way of transmitting data over multiple antennas achieving the right balance between diversity and spatial multiplexing. The Maximum Likelihood (ML) technique is a conventional way of decoding the transmitted information from the received data, but at the cost of increased complexity. The sphere decoder algorithm is a sub-optimal decoding technique that is computationally efficient achieving a ;symbol error rate that is dependent on the initial radius of the sphere. In this thesis, the decreasing rate of the radius of the sphere is increased by using a scaling factor of less than unity. This allows the algorithm to examine less number of vectors compared to the original algorithm making it much more computationally efficient. The sphere decoding algorithm is largely focused on the Alamouti codes that have two antennas at the transmitter. This work extends the sphere decoding algorithm to other STBC having more than 2 transmit and receive antennas. The performance and the computational complexity of the fast sphere decoder is compared with that of the original sphere decoder and its variants --Abstract, page iii

Optimal low-complexity detection for space division multiple access wireless systems

A symbol detector for wireless systems using space division multiple access (SDMA) and orthogonal frequency division multiplexing (OFDM) is derived. The detector uses a sphere decoder (SD) and has much less computational complexity than the naive maximum likelihood (ML) detector. We also show how to detect non-constant modulus signals with constrained least squares (CLS) receiver, which is designed for constant modulus (unitary) signals. The new detector outperforms existing suboptimal detectors for both uncoded and coded systems