Takayuki Nagayasu

Complexity reduction and performance improvement of a decision feedback equalizer for 16QAM in land mobile communications

This paper proposes a complexity-reduced decision feedback equalizer (DFE) for 16-ary quadrature amplitude modulation (16QAM) using tap gain interpolation, bi-directional equalizing (BDE) and space diversity combining (SDC) to achieve high spectral efficiency and high quality data transmission over frequency-selective fading channels in land mobile communications. To reduce the amount of computation required for BDE and SDC, we propose a tap gain interpolation scheme and pre-decision schemes for both processes. Computer simulation of a (16QAM/TDMA system) confirms that the proposed scheme improves frequency-selective fading compensation performance by 6 dB or more while using only 27% of the computation of conventional single branch DFE receivers. >

Likelihood Estimation for Reduced-Complexity ML Detectors in a MIMO System

This paper proposes a likelihood estimation method for reduced-complexity maximum-likelihood (ML) detectors in a multiple-input multiple-output (MIMO) system. Reduced-complexity ML detectors, e.g., sphere decoder (SD) and QR decomposition (QRD)-M algorithm, are very promising candidates as a MIMO detector because they can estimate the ML or quasi-ML symbol with very low computational complexity. However, they may lose likelihood information about signal vectors having the opposite bit to hard decisions. Therefore, bit error rate performances of the reduced-complexity ML detectors are inferior to that of the ML detector when soft-decision decoding is employed. This paper proposes a simple estimation method of the lost likelihood information suitable for the reduced-complexity ML detectors. Computer simulation confirms that the proposed method provides excellent decoding performance, keeping the advantage of computational cost of the reduced-complexity ML detectors.

Capacity Evaluation of MIMO-OFDM Systems using Reduced-Complexity ML Detectors in a Spatially Correlated Channel

This paper evaluates reduced-complexity maximum-likelihood (ML) detectors with soft-decision outputs in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. The reduced-complexity ML detectors, e.g., a sphere decoder (SD) or QR decomposition (QRD)-M algorithm, are very promising candidates as a MIMO detector because they can estimate the ML or quasi-ML symbol with very low computational complexity. These detectors, however, have a difficulty in producing soft-decision outputs because they may lose likelihood required for calculating log-likelihood ratio (LLR), which is related to reliability of detector outputs. We have proposed a simple likelihood estimation method which estimates the lost likelihood by means of a combination of the ML estimation and spatial-filtering method. This paper evaluates the reduced-complexity ML detectors with the proposed method in a spatially correlated MIMO channel to prove its availability in a realistic environment. Computer simulation confirms that the proposed method provides excellent decoding performance and achieves very high system capacity.