The lookup-table-based digital adaptive predistortion (DAPD-LUT) approaches are low cost and effective for power amplifier (PA) linearization in wireless applications. However, most existing DAPD-LUT schemes are sub-optimum because they adopt uniformly spaced LUTs regardless of the system state information (SSI), i.e., the PA characteristics and the input signal statistics. Other existing DAPD-LUT schemes assume either full or partial knowledge of the SSI to optimize and then to freeze the LUT spacing. Without prior knowledge of the SSI, we propose an SSI-learning low-complexity procedure to optimize the LUT spacing for a DAPD-LUT scheme. The proposed procedure is capable of online adapting the LUT spacing for PAs with various nonlinear characteristics, for input signals with various statistics, and for wireless environments with various time-varying properties.

We proposed a novel Viterbi-based algorithm using jiggling substates for joint data sequence detection, symbol boundary self-calibration, and signal frame synchronization for the EPC-Global Gen-2 system. The proposed algorithm first represents the data-encoded scheme as a trellis diagram, and then, as a consequence; the data sequence estimation can be carried out through the Viterbi algorithm. Moreover, time duration of the symbol waveform is iteratively adjusted to generate two substates in the Viterbi algorithm so as to trace and calibrate the symbol boundary on the fly. Compared with conventional approaches, the proposed Viterbi-based algorithm can significantly improve the system performance in terms of data detection accuracy due to its full exploitation of the baseband signal structure combining with the developed substate technique.

This study presents a computationally efficient maximum likelihood (ML) algorithm for estimating the carrier frequency offset (CFO) of generalized frequency division multiplexing systems. The proposed algorithm uses repetitive subsymbols and virtual carriers to estimate the fractional and integer CFOs, respectively. Through the use of repetitive subsymbols, this study first calculates the ML estimate of the fractional CFO in the time domain and then, accordingly, compensates for it from the received signal. The integer CFO can then be estimated through a virtual-carrier-mapping process in the frequency domain. In addition to improving performance in terms of estimation accuracy and computational complexity, the proposed non-data-aided algorithm is spectrally efficient relative to traditional algorithms.