Hussain Ali

Fuzzy simulated evolution algorithm for multi-objective optimization of VLSI placement

A fuzzy simulated evolution algorithm is presented for multi-objective minimization of VLSI cell placement problem. We propose a fuzzy goal-based search strategy combined with a fuzzy allocation scheme. The allocation scheme tries to minimize multiple objectives and adds controlled randomness as opposed to original deterministic allocation schemes. Experiments with benchmark tests demonstrate a noticeable improvement in solution quality.

Fuzzy simulated evolution algorithm for VLSI cell placement

Placement is a major step encountered during the design of very large scale integrated circuits. It is a generalization of the quadratic assignment problem with numerous constraints, several objectives, and a very noisy solution space. Besides the NP-hard nature of this problem, many circuit parameters such as area, interconnect delays, wire requirements, etc. can only be imprecisely estimated before completing the remaining design automation steps and committing the circuit to silicon. Further, the best placement is usually one that combines several desirable physical characteristics. There has not been a consensus on how to accommodate all these (conflicting) requirements in the search for near optimal feasible solutions. In this paper, we present a fuzzy simulated evolution (FSE) algorithm to tackle this problem. Identification of near optimal solutions is achieved through a novel goal-directed fuzzy search approach. This approach can be followed by other iterative (meta-) heuristics to find desirable solutions to optimization problems with noisy search space and possibly more than one objective. This approach is dominance preserving, i.e. if a solution A dominates another solution B with respect to all objective criteria, then A will surely have a higher membership in the fuzzy set of good solutions than solution B. Further, the approach scales well with larger problem instances and/or a larger number of objective criteria. Also, the operators of all stages of simulated evolution have been implemented using fuzzy logic to exploit the nature of fuzzy information of the problem domain. Experiments with benchmark tests demonstrate a noticeable improvement in solution quality.

Adaptive bias simulated evolution algorithm for placement

Simulated Evolution (SE) is a general meta-heuristic for combinatorial optimization problems. A new solution is evolved from current solution by relocating some of the solution elements. Elements with lower goodnesses have higher probabilities of getting selected for perturbation. Because it is not possible to accurately estimate the goodness of individual elements, SE resorts to a Selection Bias parameter. This parameter has major impact on the algorithm run-time and the quality of the solution subspace searched. In this work, we propose an adaptive bias scheme which adjusts automatically to the quality of solution and makes the algorithm independent of the problem class or instance, as well as any user defined value. Experimental results on benchmark tests show major speedup while maintaining similar solution quality.

Extended low-density parity-check codes for feedback-based cooperative diversity schemes

Cooperative diversity or user cooperation achieves the diversity gain without adding physical antennas to the users or mobile stations. The users work in a cooperative fashion by using their single antennas to create a virtual transmit diversity, called relay diversity or cooperative diversity. The diversity gain achieved by cooperative diversity can be further improved using error correction codes. Low-density parity-check (LDPC) codes are linear block codes with good error correction capabilities. The authors present a novel approach using extended LDPC codes to increase the diversity gain in cooperative diversity. The authors also compare the extended LDPC codes with the punctured LDPC codes in a cooperative diversity and show that there is a performance–complexity tradeoff between the punctured and the extended LDPC codes in the cooperative diversity. Furthermore, the authors will propose two feedback-based protocols for cooperative diversity and compare their throughput with non-feedback-based cooperative diversity using extended LDPC codes.

A novel approach for using extended LDPC codes in cooperative diversity

Cooperative diversity or user cooperation achieves the diversity gain without adding physical antennas to the users or mobile stations. The users work in cooperative fashion using their single antennas to create a virtual transmit diversity, called relay diversity or cooperative diversity. The diversity gain achieved by cooperative diversity can be further improved using error correction codes. Low-density parity-check (LDPC) codes are linear block codes with good error correction capabilities. We present a novel approach using extended LDPC codes to increase the diversity gain in cooperative diversity. We also compare the extended LDPC codes with punctured LDPC codes in cooperative diversity and show that extended LDPC codes have lesser complexity than punctured LDPC codes in cooperative diversity.

Target parameter estimation for spatial and temporal formulations in MIMO radars using compressive sensing

Conventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars require the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of received snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays, the inversion of the covariance matrix becomes computationally very expensive. Compressive sensing (CS) algorithms which do not require the inversion of the complete covariance matrix can be used for parameter estimation with fewer number of received snapshots. In this work, it is shown that the spatial formulation is best suitable for large MIMO arrays when CS algorithms are used. A temporal formulation is proposed which fits the CS algorithms framework, especially for small size MIMO arrays. A recently proposed low-complexity CS algorithm named support agnostic Bayesian matching pursuit (SABMP) is used to estimate target parameters for both spatial and temporal formulations for the unknown number of targets. The simulation results show the advantage of SABMP algorithm utilizing low number of snapshots and better parameter estimation for both small and large number of antenna elements. Moreover, it is shown by simulations that SABMP is more effective than other existing algorithms at high signal-to-noise ratio.

ARQ-based scheme for coded wireless cooperative communications

Cooperative communications have received an increasing attention in the last few years due to the spread of wireless devices. While multi-antenna systems can improve the system performance greatly, it is difficult to implement antenna arrays on hand-held devices due to size, cost and hardware limitation. Cooperation allows the users to share their single-antenna devices to create a virtual multi-antenna system that benefits from the transmit diversity. On the other hand, low density parity check (LDPC) code is a class of linear block codes that can theoretically approach the Shannon limit. In this work, we propose an automatic-repeat-request (ARQ)-based LDPC coded cooperative system that provides an improvement in both error rate and throughput.

Reduced complexity FFT-based DOA and DOD estimation for moving target in bistatic MIMO radar

In this paper, we consider a bistatic multiple-input multiple-output (MIMO) radar. We propose a reduced complexity algorithm to estimate the direction-of-arrival (DOA) and direction-of-departure (DOD) for moving target. We show that the calculation of parameter estimation can be expressed in terms of one-dimensional fast-Fourier-transforms which drastically reduces the complexity of the optimization algorithm. The performance of the proposed algorithm is compared with the two-dimension multiple signal classification (2D-MUSIC) and reduced-dimension MUSIC (RD-MUSIC) algorithms. It is shown by simulations, our proposed algorithm has better estimation performance and lower computational complexity compared to the 2D-MUSIC and RD-MUSIC algorithms. Moreover, simulation results also show that the proposed algorithm achieves the Cramer-Rao lower bound.

Reduction of snapshots for MIMO radar detection by block/group orthogonal matching pursuit

Multiple-input multiple-output (MIMO) radar works on the principle of transmission of independent waveforms at each element of its antenna array and is widely used for surveillance purposes. In this work, we investigate MIMO radar target localization problem with compressive sensing. Specifically, we try to solve the problem of estimation of target location in MIMO radar by group and block sparsity algorithms. It will lead us to a reduced number of snapshots required and also we can achieve better radar resolution. We will use group orthogonal matching pursuit (GOMP) and block orthogonal matching pursuit (BOMP) for our problem.

Incremental relaying protocols for extended LDPC coded cooperative diversity

Cooperative diversity achieves the diversity gain without adding physical antennas to the users or mobile stations. Coded cooperative diversity improves the frame-error-rate (FER) performance. The throughput of coded cooperative diversity can be enhanced by incremental relaying protocols which utilize limited feedback from the destination. In this work, we present two feedback-based protocols for extended low-density parity-check (LDPC) coded cooperative diversity framework.