Maximum likelihood detection of nonlinearly distorted OFDM signal

Nir Regev, Ilia Iofedov, Dov Wulich

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations


This paper deals with a Maximum Likelihood receiver for a nonlinearly distorted OFDM signal over a flat channel with AWGN. The nonlinearity destroys the orthogonality between subcarriers, consequently, a per subcarrier decision, used when the linear PA is considered, is no longer optimal. We propose a sub-optimal receiver based on the Maximum Likelihood (ML) criterion. The ML receiver has to find the minimum Euclidean distance between the received vector and a set of all possible OFDM symbols passed through the same nonlinearity. This approach has exponential complexity. To reduce the complexity, we propose a sub-optimal receiver that minimizes the Euclidean distance, seen as a cost function, by the gradient descent algorithm. Unfortunately, due to the nonlinearity, the cost function is non-convex. In order to overcome this obstacle, we propose a method to classify the solution, i.e., to decide if the achieved minimum is local or global. We modify the gradient descent algorithm to avoid convergence to a local minimum. It is shown that the proposed receiver outperforms the simple OFDM and iterative receivers in terms of symbol error rate (SER) performance.

Original languageEnglish
Article number7417009
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
StatePublished - 1 Jan 2015
Event58th IEEE Global Communications Conference, GLOBECOM 2015 - San Diego, United States
Duration: 6 Dec 201510 Dec 2015


  • Gradient Descent Algorithm
  • Maximum Likelihood
  • Nonlinear Power Amplifier
  • OFDM

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing


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