Joint Channel and Clipping Level Estimation for OFDM in IoT-based Networks

Abstract

We consider scenarios such as IoT-based 5G or IoT-based Machine Type Communication (MTC), where a low cost low-power transmitter communicates with a high-quality receiver. Then, digital pre-distortion of the non-linear power amplifier may be too expensive. In order to investigate the feasibility of receiver-side compensation of the transmitter RF impairments, we study joint maximum-likelihood estimation of channel and clipping level in multi-path fading OFDM systems. In particular, we propose an alternative optimization algorithm which uses frequency-domain block-type training symbols, and prove that this algorithm always converges, at least to a local optimum point. Then, we calculate the Cram´er-Rao lower bound, and show that the proposed estimator attains it for high Signal-to-Noise Ratios. Finally, we perform numerical evaluations to illustrate the performance of the estimator, and show that iterative decoding can be done using the estimated channel and clipping level with almost the same performance as a genie-aided scenario, where the channel and clipping level are perfectly known.

Existing System

Power Amplifier-Centric Technique, Maximum-Likelihood Detection.

Proposed System

In this paper, we propose algorithms to jointly estimate channel and Clipping Amplitude (CA), when a limiter (clipper) is deployed as the nonlinearity model. The importance of this model comes from the fact that it can be used in different scenarios. One way to reduce the PAPR at the transmitter side is the intentional clipping of the OFDM signal. To model this clipping, a limiter is used in the literature. Moreover, a limiter itself can be considered as a simplified yet useful model of nonlinear HPAs. Furthermore, even if a pre-distorter is used at the transmitter side the cascaded combination of that and the HPA often well approximated by a limiter. Considering low cost devices in IoT-based networks, clipping the high PAPR signal by a limiter can be a very promising approach to reduce the cost of having highly efficient HPAs. Furthermore, clipping the high PAPR signal increases the battery life, resulting in increased power efficiency of the HPA. In particular, we have proposed two alternating optimization algorithms, in which we have optimally solved a non-smooth non-convex optimization problem. We have also computed the theoretical lower bounds(CRLB) on the performance of these estimators, and showed that they attain these lower bounds. Next, we have combined the channel and the CA estimates with the iterative detection method from to perform symbol detection at the receiver. Finally, we have showed by simulations that the performance of the iterative detection method using the proposed algorithms.

Architecture

SYSTEM MODEL