Vimal Bhatia

Non-parametric likelihood based channel estimator for Gaussian mixture noise

Extensive work to develop and optimise signal processing for signals that are corrupted by additive Gaussian noise has been done so far mainly because of the central limit theorem and the ease in analytic manipulations. It has been observed that the algorithms designed for Gaussian noise typically perform poor in presence of Gaussian mixture (non-Gaussian) noise. This paper discusses a likelihood based algorithm using kernel density estimates to improve channel estimation over a block in non-Gaussian noise environments. The likelihood pdf is assumed unknown and is estimated by using kernel density estimator at the receiver. A novel technique for channel estimation using a whitening filter for interference limited channels is also proposed in this paper. The performance of the proposed estimator is compared with the Cramer Rao lower bound for associated noise distribution. The simulations for impulsive noise and co-channel interference in presence of Gaussian noise, confirms that a better estimate can be obtained by using the proposed technique as compared to the traditional least-squares-based algorithms in highly non-Gaussian environments.

Performance Analysis of Amplify-and-Forward Cooperative Networks with Best-Relay Selection Over Weibull Fading Channels

We analyze and study the performance of amplify-and-forward cooperative diversity communication network with best-relay selection over Weibull fading channel. In this paper, a closed-form expression for the moment generating function (MGF) of the total signal-to-noise ratio at the destination is derived in terms of the tabulated Meijer’s G-function. By the help of this derived MGF expression, we analyze the average symbol error rate (ASER) and outage probability over independent and identical distributed Weibull fading channels for multiple relays. The numerical values of ASER and outage probability expressions are compared with Monte-Carlo simulation result to verify the accuracy of the derivation.

Congestion control based ant colony optimization algorithm for large MIMO detection

Concept of negative pheromone is used to avoid the early convergence to a local minima.A new probabilistic search approach for detection in large MIMO systems is proposed.Improved performance under channel estimation error is achieved.Bit error rate performance improves with increase in number of antennas. Employing multiple antennas in wireless communication systems is a key technology for future generation of wireless systems. Symbol detection in multiple-input multiple-output (MIMO) systems with low complexity is challenging. The minimum bit error rate (BER) performance can be achieved by maximum likelihood (ML) detection. However, with increase in number of antennas in MIMO systems, the ML detection becomes impractical. For example, sphere decoder (SD) is a well known ML detector for MIMO systems, however because of its high complexity it is practical only up to 32 real dimensions. Recently, bio-inspired algorithms are being used for improving the BER performance of MIMO symbol detector, along with low complexity. In this article, we propose a congestion control based ant colony optimization (CC-ACO) algorithm for large MIMO detection. We also discuss the robustness of the proposed algorithm under channel state information (CSI) estimation error. The simulation results shows the effectiveness of the proposed algorithm in terms of achieving better bit error rate (BER) performance with low complexity.

A low-complexity hybrid algorithm based on particle swarm and ant colony optimization for large-MIMO detection

A low-complexity hybrid algorithm for large-MIMO detection is proposed.Hybridization of ant colony and particle swarm optimization algorithms.Superior performance over existing ant colony optimization algorithms.The hybrid algorithm achieves near optimal bit error rate performance. With rapid increase in demand for higher data rates, multiple-input multiple-output (MIMO) wireless communication systems are getting increased research attention because of their high capacity achieving capability. However, the practical implementation of MIMO systems rely on the computational complexity incurred in detection of the transmitted information symbols. The minimum bit error rate performance (BER) can be achieved by using maximum likelihood (ML) search based detection, but it is computationally impractical when number of transmit antennas increases. In this paper, we present a low-complexity hybrid algorithm (HA) to solve the symbol vector detection problem in large-MIMO systems. The proposed algorithm is inspired from the two well known bio-inspired optimization algorithms namely, particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm. In the proposed algorithm, we devise a new probabilistic search approach which combines the distance based search of ants in ACO algorithm and the velocity based search of particles in PSO algorithm. The motivation behind using the hybrid of ACO and PSO is to avoid premature convergence to a local solution and to improve the convergence rate. Simulation results show that the proposed algorithm outperforms the popular minimum mean squared error (MMSE) algorithm and the existing ACO algorithms in terms of BER performance while achieve a near ML performance which makes the algorithm suitable for reliable detection in large-MIMO systems. Furthermore, a faster convergence to achieve a target BER is observed which results in reduction in computational efforts.

Stochastic gradient algorithms for equalisation in α-stable noise

This paper addresses the problem of developing a least mean squares (LMS) style decision feedback equaliser algorithm for minimising bit error rate (BER) in impulsive noise environments characterised by the α-stable distribution. The development exploits the stable nature of the α-distribution and the concepts built on an earlier work in a Gaussian noise environment. Further, a Wiener-filter-with-limiter solution is also presented and used as a performance bench mark. An improvement in convergence and BER performance is achieved by using a minimum bit error rate (MBER) cost function instead of a conventional LMS-based design. The ability of least BER (LBER) equalisers based on a Gaussian noise assumption to operate in an α-stable noise environment is also highlighted.

Performance analysis of OFDM based AF cooperative systems in selection combining receiver over Nakagami-m fading channels with nonlinear power amplifier

In this paper, we investigate the performance of orthogonal frequency division multiplexing (OFDM) based amplify-and-forward (AF) cooperative relaying systems over independent and identically distributed (i.i.d) Nakagami-m fading channels. Specifically, we derive a closed-form expression for lower bound of outage probability by using selection combining (SC) scheme at the receiver. A nonlinear power amplifier (PA) is considered at the relay which introduces the nonlinear distortions. We present simulation and numerical results to validate the theoretical analysis, and demonstrates the impact of nonlinearity of PA parameters over outage probability for different values of threshold signal-to-noise ratio (SNR) at various mean SNR levels

Chebyshev Polynomial-Based Adaptive Predistorter for Nonlinear LED Compensation in VLC

In recent times, there has been a surge in research on visible light communications (VLC). In VLC systems, normal light emitting diode (LED) lamps are used as transmitters by modulating the optical power of the LED with the input current. All this is done at a high frequency, so that the fluctuations are not visible to the naked eye. It is a well known fact that the LED characteristics are not linear. Such nonlinearities degrade the performance of VLC. In the literature, a predistorter using a linear adaptive scaling parameter is proposed as a predistorter, which uses the well known normalized least mean squares (NLMSs) algorithm as the learning mechanism. However, to correct a nonlinearity, we need a nonlinear mapping/predistorter. This letter proposes a Chebyshev regression-based nonlinear predistorter to correct the nonlinear characteristics of LED by learning a polynomial expansion of the input electrical signal so as to mitigate the LED nonlinearity. Simulations have been carried out to validate the performance of the algorithm against existing adaptive predistortion techniques, such as NLMS-based predistortion and post-distortion techniques, such as Volterra and Hammerstein filters.

Outage Analysis of OFDM Based AF Cooperative Systems in Selection Combining Receiver over Nakagami-m Fading Channels with Nonlinear Power Amplifier

In this paper, we investigate the performance of orthogonal frequency division multiplexing (OFDM) based amplify-and-forward (AF) cooperative relaying systems over independent and identically distributed (i.i.d) Nakagami-

Mitigating NLD for Wireless Networks: Effect of Nonlinear Power Amplifiers on Future Wireless Communication Networks

m

Improved multiple feedback successive interference cancellation algorithms for near-optimal MIMO detection

fading channels. Specifically, we derive a closed-form expression for lower bound of outage probability by using selection combining (SC) scheme at the receiver. A nonlinear power amplifier (PA) is considered at the relay which introduces the nonlinear distortions. We present simulation and numerical results to validate the theoretical analysis, and demonstrates the impact of nonlinearity of PA parameters over outage probability for different values of threshold signal-to-noise ratio (SNR) at various mean SNR levels