Pei-Jung Chung

Base Station Location Optimization for Minimal Energy Consumption in Wireless Networks

This paper studies the combined problem of base station location and optimal power allocation, in order to optimize the energy efficiency of a cellular wireless network. Recent work has suggested that moving from a network of a small number of high power macrocells to a larger number of smaller microcells may improve the energy efficiency of the network. This paper investigates techniques to optimize the number of base stations and their locations, in order to minimize energy consumption. An important contribution of the paper is that it takes into account non-uniform user distributions across the coverage area, which is likely to be encountered in practice. The problem is solved using approaches from optimization theory that deal with the facility location problem. Stochastic programming techniques are used to deal with the expected user distributions. An example scenario is presented to illustrate how the technique works and the potential performance gains that can be achieved.

A Probabilistic Constraint Approach for Robust Transmit Beamforming With Imperfect Channel Information

Transmit beamforming (or precoding) is a powerful technique for enhancing performance of wireless multiantenna communication systems. Standard transmit beamformers require perfect channel state information at the transmitter (CSIT) and are sensitive to errors in channel estimation. In practice, such errors are inevitable due to finite feedback resources, quantization errors and other physical constraints. Hence, robustness has become a crucial issue recently. Among two popular robust designs, the stochastic approach exploits channel statistics and optimizes the average system performance while the maximin approach considers errors as deterministic and optimizes the worst case performance. The latter usually leads to a very conservative design against extreme (but rare) conditions which may occur at a very low probability. In this paper, we propose a more flexible approach that maximizes the average signal-to-noise ratio (SNR) and takes the extreme conditions into account using the probability with which they may occur. Simulation results show that the proposed beamformer offers higher robustness against channel estimation errors than several popular transmit beamformers.

Exploiting Negative Feedback Information for One-Bit Feedback Beamforming Algorithm

In this paper a hybrid one-bit feedback algorithm is proposed to achieve carrier phase alignment at the receiver for distributed transmit beamforming. The proposed iterative algorithm employs two schemes to speed up the convergence process, which exploit negative feedback information in a single time slot (Scheme 1) and in successive time slots (Scheme 2) respectively, whereas previously proposed algorithms in the literature discard this information. We show that the proposed algorithm yields a significant improvement in the convergence speed compared to the original algorithm. Furthermore, we modify the proposed algorithm to be capable of tracking time-varying channels which have variable rates of phase drift. The modified hybrid algorithm has the ability to adjust perturbation sizes adaptively without the knowledge of channel state information and is suited for practical implementations.

Maximum likelihood array calibration using particle swarm optimisation

Calibration of array shape error is a key issue for most existing source localisation algorithms. In this study, the far-field self-calibration and near-field pilot-calibration are carried out using unconditional maximum likelihood (UML) estimator whose objective function is optimised by particle swarm optimisation (PSO). A new technique, decaying diagonal loading (DDL), is proposed to enhance the performance of PSO at high signal-to-noise ratio (SNR) by dynamically lowering it, based on the counter-intuitive observation that the global optimum of the UML objective function is more prominent at lower SNR. Numerical simulations demonstrate that the UML estimator optimised by PSO with DDL is robust to large shape errors, optimally accurate and free of the initialisation problem. In addition, the DDL technique can be coupled with different global optimisation algorithms for performance enhancement. Mathematical analysis indicates that the DDL is applicable to any array processing problem where the UML estimator is employed.

An F-Test Based Approach for Spectrum Sensing in Cognitive Radio

Spectrum sensing is a key task in cognitive radio networks. Traditional sensing techniques such as energy detector suffer from noise uncertainty problem or require high computational complexity. In this paper, we propose a novel sensing technique using F-test by considering a multiple antenna cognitive radio system. This method is insensitive to noise uncertainty and easy to implement. It requires the channel state information (CSI) as prior knowledge. Based on statistical properties of F-distribution, we shall derive the test threshold and probability of detection, respectively. In addition, the performance of the proposed approach under imperfect channel information will be discussed. Simulation results show that the proposed F-test based detector achieves significant performance improvement compared with several popular detectors and offers robustness against noise uncertainty.

Distributed target localization using quantized received signal strength

In this paper, we propose a distributed gradient algorithm for received signal strength based target localization using only quantized data. The Maximum Likelihood of the Quantized RSS is derived and Particle Swarm Optimization is used to provide an initial estimate for the gradient algorithm. A practical quantization threshold designer is presented for RSS data. To derive a distributed algorithm using only the quantized signal, the local estimate at each node is also quantized. The RSS measurements and the local estimate at each sensor node are quantized in different ways. By using a quantization elimination scheme, a quantized distributed gradient method is proposed. In the distributed algorithm, the quantization noise in the local estimate is gradually eliminated with each iteration. Section 5 shows that the performance of the centralized algorithm can reach the Cramer Rao Lower Bound. The proposed distributed algorithm using a small number of bits can achieve the performance of the distributed gradient algorithm using unquantized data. HighlightsThe quantized distributed gradient method is applied for target localization.Particle swarm optimization is used to provide an initial estimate.A quantization error elimination scheme reduces communication cost.A quantization method for RSS measurements is presented.

Near-field array shape calibration

In the important domain of array shape calibration, the near-field case poses a challenging problem due to the array response complexity induced by the range effect. In this paper, near-field calibration is carried out using an unconditional maximum likelihood (UML) estimator. Its objective function is optimized by the particle swarm optimization (PSO) algorithm. A new technique, decaying diagonal loading (DDL) is proposed to enhance the performance of PSO at high signal-to-noise ratio (SNR) by dynamically lowering it, based on the counter-intuitive observation that the global optimum of the UML objective function is more prominent at lower SNR. UML estimator offers Cramér-Rao bound (CRB)-attaining accuracy. The direct optimization by PSO without approximation makes the estimator applicable to the entire near-field. In addition, PSO is free of the initialization problem from which the local optimization algorithms suffer. Numerical simulations demonstrate the CRB-attaining results at SNR as high as 60 dB.

Determining the Number of Propagation Paths from Broadband Mimo Measurements via Bootstrapped Likelihoods and the False Discovery Rate Criterion - Part II: Application

In this contribution, we apply a multiple hypothesis test procedure to real broadband MIMO antenna array measurements recorded with a RUSK-ATM vector channel sounder. The RUSK-ATM vector channel sounder operated at a center frequency of 2 GHz and a bandwidth of 120 MHz. Maximum-likelihood estimates for propagation delay, direction of arrival, direction of departure are obtained with the space alternating generalized expectation-maximization (SAGE) algorithm. The Benjamini-Hochberg procedure is invoked using the bootstrap approximation to select the number of propagation paths for a given false discovery rate.