Implementation-Friendly and Energy-Efficient Symbol-by-Symbol Detection Scheme for IEEE 802.15.4 O-QPSK Receivers

Abstract

In this paper, the noncoherent detection scheme for the receiver in wireless sensor nodes is discussed. That is, an implementation-friendly and energy-efficient symbol-by-symbol detection scheme for IEEE 802.15.4 offset-quadrature phase shift keying (O-QPSK) receivers is investigated under both pure additive white Gaussian noise (AWGN) channel and fading channel. Specifically, the residual carrier frequency offset (CFO) of the chip sample is estimated and compensated with the aid of the preamble; then, the standard noncoherent detection scheme with perfectly known CFO is directly configured. The corresponding simulation results show that only 4 preamble symbols is sufficient for accurate CFO estimation. Compared with the conventional noncoherent detector, the average running time per data packet of our enhanced detector is only 0.17 times of the former; meanwhile, at the packet error rate of 1 × 10 −3 , our enhanced detector can obtain 2.2 dB gains in the (32, 4) direct sequence spread spectrum system. A more reasonable trade-off between complexity and reliability is thus achieved for energy-saving and maximum service life in wireless sensor networks (WSNs). INDEX TERMS Internet of things, IEEE 802.15.4 wireless sensor networks, offset QPSK, symbol-bysymbol detection.

Reduced-Complexity Multiple-Symbol Detection of O-QPSK Signals in Smart Metering Utility Networks

“…M is the truncated number of chips and 1 ≤ M ≤ K/2. Note that it is the SBSD scheme when j is set to be 1 [8].…”

“…In this paper, the estimation of the residual CFO is based on the detection of the signal envelope. Compared with the common space subdivision method, the adaptive CFO estimation algorithm with the triangular approximation algorithm provides a simpler space division rules and without undesirable errors [8,9]. Moreover, the complexity of the full estimator can achieve significant reduction by the triangular approximation tan −1 (x) = x and sin −1 (x) = x.…”

“…Moreover, the complexity of the full estimator can achieve significant reduction by the triangular approximation tan −1 (x) = x and sin −1 (x) = x. We can easily achieve two full but high-complexity complex expressions of arg {R (n)} as shown in (16) and (17) [8].…”

“…Considering the triangular approximation algorithm tan −1 (x) ≈ x and sin −1 (x) ≈ x in our previous work [8], we can obtain two simplified expressions of R (n) as given by Equations (18) and (19).…”

“…Note that, although there are few researches on the detection mechanism of receivers, such researches are equally important for scholars and users. This is mainly because the detection performance of receivers can directly affect the signal recovery efficiency of receivers and the transmission efficiency of WSNs nodes, and even affect the working efficiency of the entire WSNs [8].…”