On-off keying (OOK) is one of the modulation schemes for non-coherent impulse radio Ultra-wideband systems. In this paper, the utilization of the kurtosis detector (KD) and fourth power detector (FD) receivers for OOK signaling is introduced. We investigate the effect of integration interval and the optimum threshold on the performance of energy detector (ED), KD and FD receivers. The semi analytic expression of BER is obtained by using generalized extreme value distribution function for KD and FD receivers. From performance point of view, the simulation results show that FD receiver outperforms KD and ED receivers. In contrast, the sensitivity to the optimum threshold is greatly reduced in KD receiver compared to ED and FD receivers.

Impulse Radio Ultra-wideband (IR-UWB) systems are based on the transmission of pulses with very short duration [1,2]. Coherent and non-coherent receivers are commonly used in IR-UWB systems. The non-coherent receivers have low complexity implementation and are used in low cost applications. In this paper, non-coherent receivers are investigated for IR-UWB on-off keying (OOK) scheme.

Energy detector (ED) is one of the non-coherent receivers for the IR-UWB signal reception [

Kurtosis detector (KD) [

In this paper, we propose utilizing the KD and FD receivers for IR-UWB OOK signalling scheme. The approximation of optimum threshold value for symbol decision and the semi analytic BER expression are calculated from GEV distribution function for IR-UWB OOK scheme. We show that, FD receiver outperforms KD and ED receivers, and the KD receiver outperforms in high integration intervals compare to ED receiver. We also show that, KD receiver does not require optimizing integration interval, and the KD receiver has a very low sensitivity to the optimum threshold value variations compare to ED receiver.

The rest of the paper is organized as follows. In Section 2, the system model of IR-UWB OOK is presented. Sections 3 and 4 describe the conventional ED structure and the proposed KD and FD receivers’ structures for OOK scheme, respectively, for the detection of IR-UWB signals. The performance evaluation and the results are discussed in Section 5. Finally, concluding remarks are presented in Section 6.

The transmitted signal in OOK scheme can be expressed as follows

where w(t) is the UWB pulse, E_{w} is the energy of w(t), T_{b} is the symbol time and b_{i}{0,1} is the binary information bits.

Signal s(t) propagates through a multipath channel with impulse response

where L is the number of multipath components, α_{k} and τ_{k} are the gain and delay associated with the kth multipath component according to IEEE802.15.4 channel model [

where n(t) is the white Gaussian noise with power spectral density N_{0}/2, and g(t) = w(t) * h(t) is the channel response to w(t).

An energy detector employs a square device, an energy integrator and a threshold decision mechanism which are shown in

where T_{i} is the integration interval and r(t) is the received signal passing through a band pass filter.

In OOK scheme, the demodulation stage has two hypotheses

where g(t) and n(t) are the received desired signal and noise respectively. The symbol decision in receiver is made by comparing z_{ED} with a threshold value Th. If the received signal energy is lower than a threshold value, the detector decides that the transmission bit is 0. If the received signal energy is larger than a threshold value, the detector decides that the transmission bit is 1.

Hypotheses 0 and 1 have the probability density functions (PDF) p_{0}(x) and p_{1}(x), respectively. The optimum threshold value Th_{opt} is obtained by the solution of p_{0}(x) = p_{1}(x). The PDFs of p_{0}(x) and p_{1}(x) are shown to be

central and non-central chi square distribution (X^{2}) respectively [

where M = BT_{i}, Г(.) denote Euler function, B is the signal bandwidth and I_{n} is the nth Bessel function of the first kind.

In this section we propose two non-coherent receivers for IR-UWB OOK signalling scheme, by using the fourth order statistics of received signal.

The Kurtosis for random variable x is defined as

where E{} denotes the expected value of the variable. If x is a Gaussian random variable, its kurtosis is zero. If x has a subgaussian distribution, it means that the distribution of x has flatness and shorter tails relative to Gaussian distribution, its kurtosis has a negative value. If x has a supergaussian distribution, it means that the distribution of x has peakedness and longer tails relative to the Gaussian distribution, its kurtosis has a positive value. In impulse radio UWB, the received signal has a supergaussian distribution in general; therefore, its kurtosis value is too larger than zero.

Kurtosis detector is based on kurtosis value of the received signal [

In this paper we used KD receiver for OOK signaling in IR-UWB systems. In this case, the kurtosis value of the received signal is calculated in receiver as follows

and then, two hypotheses in KD receiver are defined as follows

In KD receiver case similar to ED receiver, the symbol decision is made by comparing z_{KD} with a threshold value Th_{K}:

where the optimum threshold value Th_{Kopt} is obtained by the solution of p_{K}_{0}(x) = p_{K}_{1}(x), and the functions of p_{K}_{0} (x) and p_{K}_{1}(x) are the probability density functions (PDF) of H_{K}_{0} and H_{K}_{1} respectively.

By using Maximum likelihood (ML) parameter estimation in simulations the PDFs of p_{K}_{0}(x) and p_{K}_{1}(x) can be fitted by Generalized Extreme Value (GEV) distribution density function. The GEV distribution function defined as follows

(12)

where ζ, σ, μ are the parameters of GEV distribution function that obtained from ML parameter estimation.

The semi analytic expression for BER is obtained by using GEV distribution parameters. The parameters of GEV distribution can be obtained from numerical methods in simulations. The approximation of threshold value is obtained by solving the following equation,

where (ζ_{0}, σ_{0}, μ_{0}) and (ζ_{1}, σ_{1}, μ_{1}) are the parameters of GEV distribution for hypothesis 0 and hypothesis 1 respectively. By using the approximation of threshold value (Th_{gev}), the BER expression of bit 0 can be evaluated as

and, the BER expression of bit 1 can be evaluated as

Finally, the BER can be expressed as

In [

The structure of FD receiver, which is shown in

and the two hypotheses are defined as follow

Similar to ED and KD receivers, the symbol decision in FD receiver is made by comparing z_{FD} with a threshold value Th_{F}:

where the optimum threshold value Th_{Fopt} is obtained by solving p_{F}_{0}(x) = p_{F}_{1}(x). p_{F}_{0}(x) and p_{F}_{1}(x) are the probability density functions of H_{F}_{0} and H_{F}_{1}, respectively.

In [_{F}_{0}(x) and p_{F}_{1}(x) are approximated by using Gamma distribution function. In this paper, these PDFs are approximated by GEV distribution function which have the same relations as p_{K}_{0}(x) and p_{K}_{1}(x). Results of simulation in section V show that the GEV distribution function has higher accuracy than Gamma distribution function.

The semi analytic expression of BER for FD receiver can be calculated from Equation (16). In this equation, parameters of GEV distribution for H_{F}_{0} and H_{F}_{1} are obtained by ML parameter estimation.

Simulations are done in IEEE 802.15.4a CM1 channel model [_{mds}) truncated to 200 nsec. The second derivative of Gaussian pulse is used with pulse duration T_{p} = 1.5 nsec, and the symbol duration is T_{b} = 400 nsec. The energy of the channel impulse response is normalized to have unit power gaini.e.. We also assume perfect synchronization.

Figures 4 and 5 show the accurate cumulative density functions (CDFs) and fitted GEV CDFs for H_{K}_{0} and H_{K}_{1}. Figures 6 and 7 show the accurate CDFs, fitted GEV, and fitted Gamma CDFs of H_{F}_{0} and H_{F}_{1}, respectively. The accurate CDFs are obtained by using the histogram method and GEV and Gamma approximate CDFs are obtained by using ML estimation of distribution parameters.

According to the above-mentioned figures, GEV CDFs fitted to H_{K}_{0}, H_{K}_{1}, H_{F}_{0} and H_{F}_{1} CDFs have high accuracy for different amounts of E_{b}/N_{0} and integration intervals. In FD receiver the fitted GEV and Gamma distributions of H_{F}_{0} have almost the same accuracy, while in the case of H_{F}_{1}, the fitted GEV CDF has a better accuracy than the fitted Gamma CDF.

_{i}) for sample amounts of E_{b}/N_{0} = 14 dB and E_{b}/N_{0} = 16 dB. In ED and FD receivers, there is an optimum integration interval that minimizes the BER. Increasing the amount of E_{b}/N_{0} causes this optimum value

to increase. In KD receiver, short integration intervals have a negative effect on the performance, and BER is almost constant for large values of integration intervals. For large integration intervals, the KD receiver does not require optimization of integration interval. The FD receiver outperforms ED and KD receivers for almost all amounts of integration intervals.

_{i} = T_{mds} = 200 nsec. This figure also demonstrates the BER of ED and FD for optimum integration intervals. For T_{i} = 200 nsec and BER = 10^{–3}, the KD receiver has a 1.4 dB better performance than the ED receiver and the FD receiver has a 2 dB better performance than the ED receiver. The ED receiver with optimized integration interval has a 0.2 dB better performance than the KD receiver for high values of E_{b}/N_{0}. The FD receiver with the optimum integration intervals has a better performance than ED and KD receivers in all values of E_{b}/N_{0}. For BER = 10^{–4}, the FD