Mitigation of PA Nonlinearity for IEEE 802.11ah Power-Efficient Uplink via Iterative Subcarrier Regularization

Abstract

An orthogonal frequency division multiplexing (OFDM) transmitter for 802.11ah uplink may consume unnecessarily high power due to the malicious effect of a large peak-to-average power ratio (PAPR). This is particularly a problem for client devices (CDs) under the low-power wide-area (LPWA) technology in an Internet of thing (IoT) system, where high PAPR signals may drive the power amplifier (PA) to operate with large input back-off (IBO). This article focuses on receiver-side signal compensation (SC) techniques and introduces a novel scheme called iterative subcarrier regularization (ISR), which is based on the generalization of Papoulis-Gerchberg algorithm (GPGA). We claim that the proposed scheme is completely compatible with 802.11ah as it only exploits the prior information available in the standard operations and popular system-build-in functions in the iterative signal reconstruction process. Extensive numerical evaluations demonstrate that the proposed scheme can improve PA efficiency by 4-9 dB for uplink signaling.INDEX TERMS Orthogonal frequency division multiplexing (OFDM), nonlinear noise, Papoulis-Gerchberg algorithm, peak-to-average power ratio (PAPR), 802.11ah.

Improved ISR for IEEE 802.11ah Nonlinearity Compensation via Adjustable Constellation Borders

“…The system structure of the OFDM transceiver with the ISR technique used in this study is fully consistent with that used in [19] for a fair performance comparison, whose block diagram is shown in Figure 1. At the transmitter, the information bits are modulated by M-ary quadrature amplitude modulation (QAM) with average power normalized to one.…”

“…As shown in Figure 1, the ISR is an iterative approach to compensate the lost (PA saturated) samples by updating the prior knowledge both in the data (time) and trans formed (frequency) domains [19]. The process can be summarized as following steps.…”

“…In the conventional ISR [19], the step of subcarrier regularization in (7) utilizes a typical decision region as the standard M-ary QAM to reset the symbols of data subcarriers to their closest constellation points. Such action may introduce a serious decision errors, even if a symbol only slightly exceeds the constellation borders (in Figure 2a, a received symbol belonging to the alphabet S A is wrongly demodulated to S B , where S A , S B ∈ {S 0 , .…”

“…Nevertheless, these techniques may come in handy for LPWA uplinks to support machine-type communications, which need to leave all possible complicated operations at base stations for power saving [17,18]. Based on this framework, iterative subcarrier regularization (ISR) [19] has recently been introduced for 802.11ah power-efficient uplink. ISR can compensate for the PA-distorted signals in two ways: regularizing subcarriers iteratively with deterministic prior knowledge only (ISR scheme I, ISR-I) or with both deterministic and probabilistic prior knowledge (ISR scheme II, ISR-II).…”

“…ISR can compensate for the PA-distorted signals in two ways: regularizing subcarriers iteratively with deterministic prior knowledge only (ISR scheme I, ISR-I) or with both deterministic and probabilistic prior knowledge (ISR scheme II, ISR-II). In ( [19], Figure 12), ISR-II outperforms ISR-I in terms of bit error rate (BER) and convergence under a fair channel; however, only ISR-I can further improve the BER by consuming more iterations when the signal-to-noise ratio (SNR) consistently increases.…”