A general framework for joint estimation-detection of channel, nonlinearity parameters and symbols for OFDM in IoT-based 5G networks

• Despite being a strong candidate also for future cellular networks, OFDM’s main drawback is the high peak-to-average power ratio. This requires transmitters to deploy high dynamic range power amplifiers which are difficult to manufacture and thereby expensive. It is particularly problematic in future IoT-based 5G networks, in which a lot of presumably low-cost low-power devices transmit data to a high-quality receiver. In order to make such transmitters as simple and cheap as possible, we consider receiver-side nonlinearity compensation and symbol detection. In particular, we study the problem of joint maximum-likelihood estimation-detection of channel and nonlinearity parameters and symbols using frequency-domain comb-type pilots in multi-path fading OFDM systems, and propose an iterative optimization algorithm to solve it. We also calculate the Cramér-Rao lower bound for a general type of memoryless nonlinearity and show that the proposed algorithm attains it, for high signal-to-noise ratios. We then show by numerical evaluations that the algorithm converges fast and the difference of its performance with the one of the genie-aided scenario, with perfect a priori knowledge of nonlinearity parameters and channel, is negligible.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)

Nyckelord

OFDM Nonlinearity Detection Estimation CRLB

Electrical Engineering

Elektro- och systemteknik

Publikations- och innehållstyp

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