Luis G. Barbero

Fixing the Complexity of the Sphere Decoder for MIMO Detection

A new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is presented in this paper. It performs a fixed number of operations during the detection process, overcoming the two main problems of the SD from an implementation point of view: its variable complexity and its sequential nature. The algorithm combines a novel channel matrix ordering with a search through a very small subset of the complete transmit constellation. A geometrically-based method is used to study the effect the proposed ordering has on the statistics of the MIMO channel. Using those results, a generalization is given for the structure this subset needs to follow in order to achieve quasi-maximum likelihood (ML) performance. Simulation results show that it has only a very small bit error rate (BER) degradation compared to the original SD while being suited for a fully-pipelined hardware implementation due to its low and fixed complexity.

Extending a Fixed-Complexity Sphere Decoder to Obtain Likelihood Information for Turbo-MIMO Systems

A list extension for a fixed-complexity sphere decoder (FSD) to perform iterative detection and decoding in turbo-multiple input-multiple output (MIMO) systems is proposed in this paper. The algorithm obtains a list of candidates that can be used to calculate likelihood information about the transmitted bits required by the outer decoder. The list FSD (LFSD) overcomes the two main problems of the list sphere decoder (LSD), namely, its variable complexity and the sequential nature of its tree search. It combines a search through a very small subset of the complete transmit constellation and a specific channel matrix ordering to approximate the soft- quality of the list of candidates obtained by the LSD. A simple method is proposed to generate that subset, extending the subset searched by the original FSD. Simulation results show that the LFSD can be used to approach the performance of the LSD while having a lower and fixed complexity, making the algorithm suitable for hardware implementation.

A Fixed-Complexity MIMO Detector Based on the Complex Sphere Decoder

A new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is presented in this paper. The algorithm performs a fixed number of operations to detect the symbols, independent of the noise level. The algorithm achieves this by combining a novel channel matrix preprocessing with a search through a small subset of the complete receive constellation. Simulation results show it has only a very small bit error ratio (BER) degradation compared to the original SD while being suited for a fully-pipelined hardware implementation due to its fixed complexity

Rapid Prototyping of a Fixed-Throughput Sphere Decoder for MIMO Systems

A field-programmable gate array (FPGA) implementation of a new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is presented in this paper. The algorithm overcomes the main drawback of the SD: its variable throughput, depending on the noise level and the channel conditions. Implementation results show that the algorithm is highly parallelizable and can be fully pipelined. This reduces the use of FPGA resources and results in a constant throughput, which is significantly higher than previous SD implementations at a cost of a very small bit error ratio (BER) degradation.

The Error Probability of the Fixed-Complexity Sphere Decoder

The fixed-complexity sphere decoder (FSD) has been previously proposed for multiple-input multiple-output (MIMO) detection in order to overcome the two main drawbacks of the sphere decoder (SD), namely its variable complexity and its sequential structure. Although the FSD has shown remarkable quasi-maximum-likelihood (ML) performance and has resulted in a highly optimized real-time implementation, no analytical study of its performance existed for an arbitrary MIMO system. Herein, the error probability of the FSD is analyzed, proving that it achieves the same diversity as the maximum-likelihood detector (MLD) independent of the constellation used. In addition, it can also asymptotically yield ML performance in the high-signal-to-noise ratio (SNR) regime. Those two results, together with its fixed complexity, make the FSD a very promising algorithm for uncoded MIMO detection.

Full Diversity Detection in MIMO Systems with a Fixed-Complexity Sphere Decoder

The fixed-complexity sphere decoder (FSD) has been previously proposed for multiple input-multiple output (MIMO) detection to overcome the two main drawbacks of the original sphere decoder (SD), namely its variable complexity and sequential structure. As such, the FSD is highly suitable for hardware implementation and has shown remarkable performance through simulations. Herein, we explore the theoretical aspects of the algorithm and prove that the FSD achieves the same diversity order as the maximum likelihood detector (MLD). Further, we show that the coding loss can be made negligible in the high signal to noise ratio (SNR) regime with a significantly lower complexity than that of the MLD.

Performance Analysis of a Fixed-Complexity Sphere Decoder in High-Dimensional Mimo Systems

The performance of a new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is analyzed in this paper. The algorithm performs a fixed number of operations to detect the signal, independent of the noise level. Simulation results show that it can be applied to high-dimensional MIMO systems presenting a very small bit error ratio (BER) degradation compared to the original SD. In addition, its deterministic nature makes it suitable for hardware implementation

A low-complexity soft-MIMO detector based on the fixed-complexity sphere decoder

This paper presents a soft-output version of the fixed-complexity sphere decoder (FSD) previously proposed for uncoded multiple input-multiple output (MIMO) detection. Thus, the soft-FSD (SFSD) can be used in turbo-MIMO systems to exchange extrinsic soft-information with the outer decoder. For that purpose, the SFSD generates a list of candidates that approximates that of the list sphere decoder (LSD) while containing information about all the possible bit values, removing the need for clipping. In addition, it overcomes the two problems of the LSD: its variable complexity and the sequential nature of its tree search. Simulation results show that the SFSD can be used to approximate the performance of the LSD while having a considerably lower and fixed complexity, making the algorithm suitable for hardware implementation.

Performance of the complex sphere decoder in spatially correlated MIMO channels

The use of multiple antennas at both transmitter and receiver is a promising technique for significantly increasing the capacity and spectral efficiency of wireless communication systems. In particular, spatial multiplexing techniques provide a means of increasing the data rate of the system without having to increase the transmitter power or the bandwidth. In recent years, special attention has been paid to the sphere decoder (SD) to detect spatially multiplexed signals. It provides optimal maximum likelihood (ML) performance with reduced complexity, compared to the maximum likelihood detector (MLD). An analysis of the performance of the SD in the presence of spatially correlated multiple-input multiple-output (MIMO) channels is presented. Analytical and simulation results show that, compared to suboptimal linear and nonlinear MIMO detectors, the SD suffers a complexity increase when correlation exists between the antennas at the transmitter or the receiver. In addition, a novel low-complexity channel ordering technique is introduced to reduce the complexity of the SD.

FPGA Design Considerations in the Implementation of a Fixed-Throughput Sphere Decoder for MIMO Systems

A field-programmable gate array (FPGA) implementation of a new detection algorithm for uncoded multiple input-multiple output (MIMO) systems based on the complex version of the sphere decoder (SD) is presented in this paper. It achieves quasi-maximum likelihood (ML) performance in systems where a hardware implementation of the maximum likelihood detector (MLD) is unfeasible due to its high complexity. It achieves this with a highly parallel and fully pipelined architecture. In addition, different design modifications are proposed and implemented to reduce the resource use and/or increase the throughput of the algorithm