Adaptive self-calibrating image rejection receiver

An adaptive self-calibrating image rejection receiver is described, containing a modified Weaver image rejection mixer and a Digital Image Rejection Processor (DIRP). The blind source-separation-based DIRP eliminates the I/Q errors improving the Image Rejection Ratio (IRR) without the need for trimming or use of power-hungry discrete components. Hardware complexity is minimal, requiring only two complex coefficients; hence it can be easily integrated into the signal processing path of any receiver. Simulation results show that the proposed approach achieves 75-97 dB of IRR.

A low-complexity self-calibrating adaptive quadrature receiver

In this paper digital part of a self-calibrating quadrature-receiver is described, containing a digital calibration-engine. The blind source-separation-based calibration-engine eliminates the RF-impairments in real-time hence improving the receiver's performance without the need for test/pilot tones, trimming or use of power-hungry discrete components. Furthermore, an efficient time-multiplexed calibration-engine architecture is proposed and implemented on an FPGA utilising a reduced-range multiplier structure. The use of reduced-range multipliers results in substantial reduction of area as well as power consumption without a compromise in performance when compared with an efficiently designed general purpose multiplier. The performance of the calibration-engine does not depend on the modulation format or the constellation size of the received signal; hence it can be easily integrated into the digital signal processing paths of any receiver.

Forecasting overseas visitors into the United Kingdom using continuous time and autoregressive fractional integrated moving average models with discrete data

This paper applies Gaussian estimation methods to continuous time models for modelling overseas visitors into the UK. The use of continuous time modelling is widely used in economics and finance but not in tourism forecasting. Using monthly data for 1986–2010, various continuous time models are estimated and compared to autoregressive integrated moving average (ARIMA) and autoregressive fractionally integrated moving average (ARFIMA) models. Dynamic forecasts are obtained over different periods. The empirical results show that the ARIMA model performs very well, but that the constant elasticity of variance (CEV) continuous time model has the lowest root mean squared error (RMSE) over a short period.

Inflation targeting and the consistency of monetary policy decisions in Mexico: an empirical analysis with discrete choice models

This paper examines the nature of monetary policy decisions in Mexico using discrete choice models applied to the Central Bank's explicit monetary policy instrument. We find that monetary policy adjustments in Mexico have been strongly consistent with the CB's inflation targeting strategy. We also find evidence that monetary policy responds in a forward-looking manner to deviations of inflation from the target and that observed policy adjustments exhibit asymmetric features, with stronger responses to positive than to negative deviations of inflation from the target and a greater likelihood of policy persistence during periods when monetary policy is tightened, compared with periods when policy is loosened.