Akihiro Shibuya

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.

Performance of TDMA slot synchronisation for mobile broadband systems in 40 GHz band radio channel

This paper addresses propagation measurements in indoor environments using the 40 GHz radio frequency and a performance of TDMA slot synchronisation under mobile broadband systems. The empirical data indicate that temporal fluctuations were observed to fit a Rician distribution in line of sight environments, on the other hand Rician and log-normal distributions were observed in obstructed radio path environments. The performance of initial acquisition for TDMA slot synchronisation was also evaluated. It shows that multipath fading gave the performance degradation when the direct paths were obstructed even if the received power level was sufficient.

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.