Design and low-power implementation of an adaptive image rejection receiver

This paper deals with and details the design and implementation of a low-power; hardware-efficient adaptive self-calibrating image rejection receiver based on blind-source-separation that alleviates the RF analog front-end impairments. Hybrid strength-reduced and re-scheduled data-flow, low-power implementation of the adaptive self-calibration algorithm is developed and its efficiency is demonstrated through simulation case studies. A behavioral and structural model is developed in Matlab as well as a low-level architectural design in VHDL providing valuable test benches for the performance measures undertaken on the detailed algorithms and structures.

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.

On the structure, convergence and performance of an adaptive I/Q mismatch corrector

The I/Q mismatches in quadrature radio receivers results in finite and usually insufficient image rejection, degrading the performance greatly. In this paper we present a detailed analysis of the Blind-Source Separation (BSS) based mismatch corrector in terms of its structure, convergence and performance. The results indicate that the mismatch can be effectively compensated during the normal operation as well as in the rapidly changing environments. Since the compensation is carried out before any modulation specific processing, the proposed method works with all standard modulation formats and is amenable to low-power implementations.

Adaptive digital receivers for analog front-end mismatch correction

Phase and gain mismatches between the I and Q analog signal processing paths of a quadrature receiver are responsible for the generation of image signals which limit the dynamic range of a practical receiver. In this paper we analyse the effects these mismatches and propose a low-complexity blind adaptive algorithm to minimize this problem. The proposed solution is based on two, 2-tap adaptive filters, arranged in Adaptive Noise Canceller (ANC) set-up. The algorithm lends itself to efficient real-time implementation with minimal increase in modulator complexity.

Design of a power-aware digital image rejection receiver

This paper deals with and details the design of a power-aware adaptive digital image rejection receiver based on blind-source-separation that alleviates the RF analog front-end impairments. Power-aware system design at the RTL level without having to redesign arithmetic circuits is used to reduce the power consumption in nomadic devices. Power-aware multipliers with configurable precision are used to trade-off the image-rejection-ratio (IRR) performance with power consumption. Results of the simulation case studies demonstrate that the IRR performance of the power-aware system is comparable to that of the normal implementation albeit degraded slightly, but well within the acceptable limits.

Adaptive-threshold region merging via path scanning

Region merging algorithms commonly produce results that are seen to be far below the current commonly accepted state-of-the-art image segmentation techniques. The main challenging problem is the selection of an appropriate and computationally efficient method to control resolution and region homogeneity. In this paper we present a region merging algorithm that includes a semi-greedy criterion and an adaptive threshold to control segmentation resolution. In addition we present a new relative performance indicator that compares algorithm performance across many metrics against the results from human segmentation. Qualitative (visual) comparison demonstrates that our method produces results that outperform existing leading techniques.