Compressive Sensing Based High-Resolution Channel Estimation for OFDM System

ABSTRACT:

Orthogonal frequency division multiplexing (OFDM) is a technique that will prevail in the next-generation wireless Communication. Channel estimation is one of the key challenges in OFDM, since high-resolution channel estimation can significantly improve the equalization at the receiver and consequently enhance the Communication performances. In this paper, we propose a system with an asymmetric digital-to-analog converter/analog-to-digital converter (DAC/ADC) pair and formulate OFDM channel estimation as a compressive sensing problem. By skillfully designing pilots and taking advantages of the sparsity of the channel impulse response, the proposed system realizes high-resolution channel estimation at a low cost. The pilot design, the use of a high-speed DAC and a regular-speed ADC, and the estimation algorithm tailored for channel estimation distinguish the proposed approach from the existing estimation approaches. We theoretically show that in the proposed system, a $N$-resolution channel can be faithfully obtained with an ADC speed at $M=O(S^{2}log(N/S))$, where $N$ is also the DAC speed and $S$ is the channel impulse response sparsity. Since $S$ is small and increasing the DAC speed to $N>M$ is relatively cheap, we obtain a high-resolution channel at a low cost. We also present a novel estimator that is both faster and more accurate than the typical $ell_{1}$ minimization. In the numerical experiments, we simulated various numbers of multipaths and different SNRs and let the transmitter DAC run at 16 times-
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the speed of the receiver ADC for estimating channels at the 16$times$ resolution. While there is no similar approaches (for asymmetric DAC/ADC pairs) to compare with, we derive the Cramér–Rao lower bound.

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