Mehmet Kemal Ozdemir

Channel estimation for wireless ofdm systems

Techniques are described for efficiently estimating and compensating for the effects of a communication channel in a multi-carrier wireless communication system. The techniques exploit the fact that the transmitted symbols are drawn from a finite-alphabet to efficiently estimate the propagation channel for multi-carrier communication systems, such systems using OFDM modulation. A transmitter transmits data through a communication channel according to the modulation format. A receiver includes a demodulator to demodulate the data and an estimator to estimate the channel based on the demodulated data. The channel estimator applies a power-law operation to the demodulated data to identify the channel. The techniques can be used in both blind and semi-blind modes of channel estimation.

Dynamics of spatial correlation and implications on MIMO systems

The use of multiple antennas has found various applications in the area of wireless communications. One such application has recently become very popular and is referred to as the multiple-input multiple-output (MIMO) antenna system. The main idea behind MIMO is to establish independent parallel channels between multiple transmit and receive antennas. Each channel uses the same frequency, and the transmissions occur simultaneously. In such a configuration, the amount of data transmitted increases linearly with the number of parallel channels, which is what makes MIMO so popular in the wireless world. The enormous capacity offered by MIMO systems is not realizable when the parallel channels are highly correlated. The goal of this article is to highlight the correlation concept and its impact on MIMO systems. Although correlation can be defined in many dimensions, here we focus on spatial correlation, and specifically consider antenna correlations in mobile units. We provide an overview of spatial correlation and present its underlying parameters in detail. Special attention is given to mutual coupling since it has signal decorrelation and antenna gain reduction effects. We then present how correlation in a MIMO system affects the amount of data that can be transmitted (MIMO capacity) and briefly review how power should be distributed with the knowledge of correlation. Analyses indicate that in real propagation environments, the high capacity gain of MIMO systems can be realized with improved antenna selection algorithms and power allocation strategies.

Initial Ranging for WiMAX (802.16e) OFDMA

Ranging is one of the most important processes in the new mobile WiMAX standard. Power adjustment, timing offset estimation, and synchronization between a base station (BS) and all users within a cell are done through the ranging process known as initial ranging. In this paper we discuss the details of initial ranging and some of the proposed algorithms as well as a novel algorithm to carry out a successful ranging process. Performance curves and computational complexity comparisons will be presented. It is observed that the proposed algorithm offers a better trade-off between computational complexity and performance

Mutual coupling effect in multiantenna wireless communication systems

A thorough investigation of mutual coupling effect on wireless communication systems is presented. Multiple antennas utilized by adaptive antenna arrays, spatial diversity structures, and multiple input-multiple output (MIMO) systems are analyzed for this purpose. In adaptive antenna arrays, mutual coupling can deteriorate the algorithms for getting the direction of arrival (DOA) and beamforming structure. In spatial diversity and MIMO systems, mutual coupling can increase or decrease the correlation between individual subchannels depending on the antenna configurations and the environment. An extensive mutual coupling model is needed to better analyze its effect on these systems. Available models either do not reflect all the parameters affecting mutual coupling or are not separable to the individual terms for a better analysis. In this paper, it is shown that mutual coupling can be expressed in terms of individual couplings due to antenna array geometry, near-field scatterers (NFS), and DOA. For MIMO and spatial diversity, DOA dependency drops since the feeding network does not have to be changed for a given DOA. By including the effect of NFS into the mutual coupling expression, the effect of mutual coupling on wireless communication systems can be better studied.

On the correlation analysis of antennas in adaptive MIMO systems with 3-D multipath scattering

The performance of multiple-input multiple-output (MIMO) systems improves substantially with the adaptation techniques like power allocation and antenna selection. These algorithms can he enhanced by considering characteristics of spatial correlation. In this study, the spatial correlation in MIMO systems is investigated to fully expose the parameters affecting it. The correlation analysis is performed by taking the effect of angle-of-arrival (AoA) statistics in three-dimensional (3-D) multipath scattering, mutual coupling, and near-field scatterers (NFS). The analysis shows that a valid model of the AoA statistics is the key parameter in the correlation analysis. Mutual coupling analysis for patch antennas reveals that coupling can reduce the correlation between antenna elements. On the other hand, the analysis on NFS suggest continuous correlation estimation. Fortunately, the rate of change of NFS is much slower than the MIMO channel variation. It is concluded that the performance of MIMO systems can be improved with accurate AoA models and tracking correlation changes.

Toward real-time adaptive low-rank LMMSE channel estimation of MIMO-OFDM systems

For the practical LMMSE channel estimation of MIMO-OFDM systems, the high computational complexity of LMMSE needs to be eliminated. In this study, we develop a low-complexity, generalized low-rank LMMSE channel estimator for MIMO-OFDM systems using comb-type pilots. The estimator utilizes the relationship between the subspaces of the frequency domain channels at the pilots and non-pilots subcarriers. By using subspace tracking, it is observed that practical LMMSE channel estimation of MIMO-OFDM systems can be feasible

IQ Imbalance Correction for OFDMA Uplink Systems

Direct conversion receivers are attractive for low cost systems as they avoid intermediate frequency (IF) filters. However, the direct conversion from radio frequencies (RF) to complex inphase (I) and quadrature (Q) baseband signals in one mixing step introduces additional front-end distortions. These IQ distortions lead to a degradation in the system performance. The problem becomes more significant in orthogonal frequency division multiple access (OFDMA) systems where multiple user signals with different IQ impairments are combined in the uplink (UL) signal. In this paper, detection methods for OFDMA-UL signals corrupted by IQ distortions are investigated. The received signal as a function of transmitted signals, IQ parameters, and communication channels is mathematically formulated. We designed a novel pilot pattern that is used by two proposed estimation and compensation methods of IQ impairments to the signal. Proposed methods were shown to significantly improve the system performance.

An efficient initial ranging algorithm for WiMAX (802.16e) OFDMA

Ranging is one of the most important processes in the mobile WiMAX standard. Power adjustment, timing offset estimation, and synchronization between a base station (BS) and all users within a cell are done through the ranging process referred to as initial ranging. In this paper, we discuss the details of initial ranging together with some of the proposed algorithms, as well as a novel algorithm to carry out a successful ranging process. Performance curves and computational complexity comparisons are presented. The system performance is evaluated for both additive white Gaussian noise (AWGN) channels and practical multipath fading channels with multiuser interference. It is shown that the proposed algorithm offers a reduced complexity ranging method that can be employed in practical WiMAX-based BSs.

A mutual coupling model for MIMO systems

Wireless communication systems employing multielement antennas (MEAs) are affected by mutual coupling. Accurate and simple models of mutual coupling are essential for better system analysis. In a MEA, mutual coupling depends on the antenna element separation, geometry of array and antenna elements, location of antenna elements in the array, frequency, substrate thickness and constant, near-field scatterers, and direction of arrival (DOA) of the incoming wave. Except near-field scatterers and DOA, the rest of the parameters are fixed for a given MEA system. Since the feeding network for a MIMO system is fixed and most of the mutual coupling is due to surface waves, DOA dependency can be removed for a MIMO mutual coupling model. In this paper, a previously proposed model for mutual coupling is modified to be applicable to MIMO systems. The effect of near-field scatterers on the mutual coupling is specifically investigated to validate the model. Commercially available EM simulators are utilized for this purpose. Once the effect of near-field scatterers is characterized, correlation properties between antenna elements in a MEA can be obtained more accurately and correlation estimation algorithms can be developed accordingly.

A narrowband MIMO channel model with 3-D scattering

This paper presents a channel model for multiple input multiple output (MIMO) systems in the presence of three-dimensional (3-D) scattering. The model combines improved sum-of-sinusoids simulation models proposed for Rayleigh fading channels, and the two-dimensional (2-D) models proposed for MIMO systems, in a 3-D scattering environment. The distribution of 3-D scattering is assumed to be uniform in horizontal plane and Gaussian in the elevated plane. Simulation results showed that the new model satisfies the desired statistics with less Doppler spread, and hence can be used to generate independent fading channels for MIMO systems. The generated channels can be further processed to obtain correlated MIMO channels through the exploitation of correlation matrices at the transmit and receive sides. The resultant channel can then he used to simulate MIMO systems for a better system performance prediction.