# ROAD Statistics-Based Noise Detection for DME Mitigation in LDACS
## 2 Related Work  
Several interference mitigation techniques for LDACS are available in the literature; among these, most of the proposed schemes focus on mitigating DME interference. In 2011, two methods capable of detecting and mitigating DME interference were put forward. The monotonous structure and spectral shape of the DME pulse are exploited in proposing these two methods. The methods achieve the merit of simpleness at the cost of losing a portion of data resulting from pulse blanking [16]. In 2012, [PERSON] et al. put forward a conoction of pulse blanking and notch filter to reduce the interference. Though the method gives leeway in the time and frequency domain of operation, it is more safe and effective for the B2 band (1900 MHz) signal [17]. Further, [PERSON] et al. proposed a DME mitigation scheme that advantages complementary code keying (CCK). Though the CCK encoding has the merit of better gain, it requires low phase distortion and a wideband channel. The latency of the system is high due to the large acquisition time. Moreover, the modulation employed here is not a power-efficient modulation [18]. In 2014, [PERSON] et al. proposed an iterative receiver design [19]. The design employs iterative decoding between the demodulator and decoder based on the Turbo principle. Another type of selective pulse blanking method to curtail DME interference is put forward in [20]. The method bestowed a designed fast filter bank for this purpose. In 2016, [PERSON] et al. propounded a method based on deformed pulse pair spotting and its subtraction from the actual signal [21]. Later, [PERSON] and [PERSON] insinuated lattice signal sets to resist DME interference. Precoding based on lattice signal set at the transmitter changes the shape of the DME signal spectrum. A simple clipping technique is then applied for DME mitigation [22]. The major flaw of pulse blanking is recognized as intercarrier interference. In [9], decision-directed noise estimation is put forward to eliminate the intercarrier interference. Figure 2: Interference Between DME and LDACS [15]. Reduced throughput of transmission and wastage of power are the drawbacks of this scheme. LDACS-OFDM based on discrete wavelet transform (DWT) is discussed in [23]. The scheme makes use of the real nature of DME. The signal affected with DME is selectively transmitted through the quadrature channel. With the effective utilization of direct sequence spread spectrum to combine the in phase and quadrature phase, the method proclaims the elimination of DME. The computational complexity and resource requirement are the flaws present in this scheme. In [24], an energy-based DME detector has been proposed. The detector works with adaptive threshold value in order to obtain the best trade-off between DME signal detection and false alarm. In 2021, a deep clipping-based DME noise reduction technique is propounded in [25]. It is a linear clipping method that uses two threshold levels for recognition and reduction in DME. Detailed study of the existing interference mitigation techniques in LDACS exposes active research is going on in this area. The Genie-aided estimator (GAE) taps the statistical description of the side information to generate the design parameters to accomplish lower bounds on the bit error at the receiver [26]. However, details of the side information are required to accomplish this lower bound performance. The correlation of the impulsive noise or the frequency in impulsive noise arrival time is some other side information [27]. When a Gaussian source is influenced by uncorrelated impulse noise, it is possible to attain optimum system performance with the use of a Bayesian signal estimator. In 2013, [PERSON] proposed an optimal Bayesian estimator (OBE) mainly for real-valued Gaussian mixture noise [28]. Later, the method was further upgraded for complex signals in 2015 [29]. It is possible to propose different types of pulse peak attenuators and pulse peak limiters for DME mitigation with the estimation knowledge obtained from GAE and OBE [30].