6 resultados para Adaptive performance

em WestminsterResearch - UK


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In this paper, we carry out a detailed performance analysis of the blind source separation based I/Q corrector operating at the baseband. Performance of the digital I/Q corrector is evaluated not only under time-varying phase and gain errors but also in the presence of multipath and Rayleigh fading channels. Performance under low-SNR and different modulation formats and constellation sizes is also evaluated. What is more, BER improvement after correction is illustrated. The results indicate that the adaptive algorithm offers adequate performance for most communication applications hence, reducing the matching requirements of the analog front-end enabling higher levels of integration.

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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.

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In this paper, the performance and convergence time comparisons of various low-complexity LMS algorithms used for the coefficient update of adaptive I/Q corrector for quadrature receivers are presented. We choose the optimum LMS algorithm suitable for low complexity, high performance and high order QAM and PSK constellations. What is more, influence of the finite bit precision on VLSI implementation of such algorithms is explored through extensive simulations and optimum wordlengths established.

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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.

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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.

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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.