Khodr A. Saaifan

Decision Boundary Evaluation of Optimum and Suboptimum Detectors in Class-A Interference

The Middleton Class-A (MCA) model is one of the most accepted models for narrow-band impulsive interference superimposed to additive white Gaussian noise (AWGN). The MCA density consists of a weighted linear combination of infinite Gaussian densities, which leads to a non-tractable form of the optimum detector. To reduce the receiver complexity, one can start with a two-term approximation of the MCA model, which has only two noise states (Gaussian and impulsive state). Our objective is to introduce a simple method to estimate the noise state at the receiver and accordingly, reduce the complexity of the optimum detector. Furthermore, we show for the first time how the decision boundaries of binary signals in MCA noise should look like. In this context, we provide a new analysis of the behavior of many suboptimum detectors such as a linear detector, a locally optimum detector (LOD), and a clipping detector. Based on this analysis, we insert a new clipping threshold for the clipping detector, which significantly improves the bit-error rate performance.

Efficient Nonlinear Detector of Binary Signals in Rayleigh Fading and Impulsive Interference

The Middleton Class-A (MCA) model is one of the most widely applied models for narrow-band impulsive interference superimposed to additive white Gaussian noise (AWGN). The MCA noise process consists of an infinite number of Gaussian-distributed noise states with different variances. As a result, the optimum detector has irreducible form. Here, our analysis is based on a two-state model, where we further approximate it to a single noise state. Therefore, a log-function reduces the likelihood ratio test (LRT) to a closed-form expression. Since the low-pass equivalent of the noise process can be expressed by in-phase and quadrature (IQ) components. We derive the nonlinear decision rules when the IQ components of noise are independent and identically distributed (i.i.d.). Furthermore, for jointly distributed IQ noise components, we show that the conventional coherent detector over a fading channel with Gaussian noise is still optimum for impulse noise.

A nonlinear diversity combiner of binary signals in the presence of impulsive interference

A Middleton Class-A (MCA) model is one of the most accurate statistical-physical models for narrowband impulse noise. The previous studies show that time diversity can efficiently be used to reduce the impact of MCA noise. The optimum combiner in such noise consists of a nonlinear preprocessor followed by a conventional combiner. Since an MCA noise process consists of an infinite number of noise states, there is no closed-form solution of the optimum nonlinearity. In this paper, we adopt a two-term model for the MCA process, which is further approximated to a simpler noise model. Therefore, we introduce a closed-form approximation of the optimum nonlinearity in the presence of real-valued MCA noise. In fading channels, we use a complex extension of an MCA model. We show how the nonlinearity operation maintains the diversity advantage in such a noise model.

A receiver design for MIMO systems over rayleigh fading channels with correlated impulse noise

A Middleton Class-A (MCA) density is well known to model impulsive interference. The statistical-physical extension of this model for multiple receive antennas is currently limited to two antennas. An algebraic extension of the univariate MCA model leads to a multivariate MCA distribution, which can be used for an arbitrary number of receive antennas. Since recent studies show a significant level of noise correlation in several wireless systems, we develop MEMO receivers for Rayleigh fading channels in the presence of spatially correlated MCA interference. We derive an upper bound pairwise error probability (PEP) for orthogonal space time block codes (OSTBCs). We show that the performance improvement of OSTBCs is highly dependent on the impulse noise environment and it becomes minor as the number of transmit and receive antennas increases. In the design of MEMO receivers, the maximum likelihood (ML) detection has a high computational complexity. Since the MCA model can be seen as a multivariate Gaussian distribution conditioned on the knowledge of noise state, we introduce a simple approach to estimate the state of noise at the receiver, which subsequently reduces the complexity of the ML decision rule.

Lattice Signal Sets to Combat Pulsed Interference from Aeronautical Signals

The inlay approach for the Broadband Aeronautical Multicarrier Communications (B-AMC) system is exposed to severe interference from the adjacent channels of the distance measuring equipment (DME) system. Here, we propose a simple technique to mitigate DME interference and subsequently enable transmission in the spectral gaps of the DME channels. The proposed technique uses a precoding at the transmitter based on employing lattice signal sets in order to modify the shape of the DME signal spectrum. Hereto, a simple clipping method is applied to the received subcarriers to mitigate the impact of the DME interference. Simulations show that the proposed subcarrier clipping technique can considerably reduce the effect of the DME signal by choosing appropriate clipping thresholds. They have been selected to maximize the signal-to interference-and-noise ratio (SINR) after the clipping operation. It has also been confirmed by simulations that the proposed method offers a significantly better performance when compared to current mitigation techniques.

Cancelation of Distance Measuring Equipment Interference for Aeronautical Communications

The L-band digital aeronautical communications system (L-DACS1) is subject to strong interference caused by distance measuring equipment (DME). For efficient statistical processing of interference, we adopt a Gaussian mixture (GM) distribution to model the impulsive nature of DME signals. Hence, we drive the parameters of the GM model in terms of properties of DME signals. This allows us to redesign the optimum receiver for mitigating DME interference. We also provide a simple pulse detector to estimate the presence of DME signals utilizing the null subcarriers of the L-DACS1.

Prioritized Adaptive Modulation for MIMO-OFDM Using Pre-Ordered SIC

In MIMO transmission, channel state information (CSI) is crucial for achieving channel adaptation. However, the inaccuracy of CSI may induce severe interferences. Hereto, limitations of linear equalizers to combat severe interference and noise enhancements necessitate the need for investigating non-linear schemes. Thus, we propose a modified successive interference cancellation (SIC) technique based on the well-known V-BLAST non-linear spatial equalizer. First, we implement a linear pre-processing filter in order to pre-sort the eigenchannels at the transmitter. This simplifies the complexity of non-linear equalization significantly by reducing the effort needed for sorting at the receiver. To protect the strong eigenbeams against errors and minimize the SIC error propagation, an unequal-error protection (UEP) bit-loading algorithm is used. A comparison to an MMSE linear equalization shows that our design operates at a lower symbol-error ratio (SER) with almost identical complexity.

A spatial diversity reception of binary signal transmission over Rayleigh fading channels with correlated impulse noise

A Class-A density is well known to model interference, which is impulsive by nature. This model is expressed as a weighted infinite linear combination of Gaussian densities with different variances. The extension of this model for multiple receiving antennas is currently limited to two antennas. An algebraic extension leads to a multivariate Class-A density, which can be used for an arbitrary number of antennas. In this paper, we consider the design of optimum diversity combining for Rayleigh fading channels in the presence of Class-A interference. Since recent studies show a significant level of noise correlation in some wireless systems, we begin with a correlated multivariate Class-A model. Then, we show that the optimum combiner can be approximated by a maximum ratio combiner (MRC) preceded by noise decorrelators, which has a much lower complexity compared with the optimum one. When the interference is uncorrelated, we prove that the conventional MRC approximates the optimum combining.

A linear piecewise suboptimum detector for signals in class-A noise

In this paper, we consider the detection problem of binary signals corrupted by Class-A interference for two observations per symbol. The Class-A density contains infinitely many terms of scaled Gaussian-mixture densities, which yields an optimum detector that requires a high computational complexity. The linear (Gaussian) detector can be used, but it suffers from a significant performance degradation in stong impulse environments. The main objective of this paper is to design a simple detector with optimum performance. We start from the optimum decision boundaries, where we propose a piecewise linear approximation for nonlinear regions. As a result, we introduce a novel piecewise detector, which has much less complexity compared with the optimum one. Simulation results show a near-optimal performance for the proposed detectors in different impulse channel environments. Moreover, we show that one and two piecewise linear approximation per each nonlinear region is sufficient to approach the optimum performance.