The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this study, the design of a superimposed training (ST) for an interference-limited spatially correlated multiple-input–multiple-output (MIMO) system is addressed and a closed-form solution for designing the training signal is proposed in a sub-optimal way. Earlier papers have considered noise limited MIMO systems. The authors also propose random/orthogonal variable spread factor (OVSF) codes as...
An iterative training sequence design scheme called Iterative SuperImposed training sequence design with Multiple Interferers, or ISIMI, is proposed for estimating MIMO channels with colored noise. The proposed approach decomposes the MIMO channel and design the training sequence on a per channel basis, thus making the use of sequential minimum mean-squared error (MMSE) estimator ideal for channel...
In this paper, we derive a performance comparison between two training-based schemes for multiple-input multiple-output systems. The two schemes are the time-division multiplexing scheme and the recently proposed data-dependent superimposed pilot scheme. For both schemes, a closed-form expression for the bit error rate (BER) is provided. We also determine, for both schemes, the optimal allocation...
A modified MIMO channel estimation method based on superimposed training sequence was proposed for the frequency selective channel. By exploiting the uncorrelation between the training and information sequences, accurate primary channel parameters could be obtained without loss of bandwidth, and using weight factor averaged neighboring channel parameters. This mew method was presented to track the...
It is conventionally supposed that the periodicity of superimposed training (ST) sequence designed for channel estimation has no impact on the ST system's performance, as long as the channel identification condition is satisfied. Accordingly the shortest sequence period equal to channel length is always preferred in ST-OFDM systems for its computational tractability. However, with additional consideration...
In this paper, A Recursive Least Squares (RLS) channel estimator with improved decision-directed algorithm (referred as DDA2-RLS) is proposed based on the superimposed training sequence in orthogonal frequency division multiplexing (OFDM) systems. The DDA2-RLS is exploited to further eliminate the interference driven by the superimposed unknown information data. Then, the theoretical analysis for...
In this work an iterative time domain least squares (LS) based channel estimation method using superimposed training (ST) for an orthogonal frequency division multiplexing (OFDM) system over frequency selective fading channels is proposed for the IEEE 802.16e 2005 standard. The estimate of the channel is generalized to provide scope for exploiting the coherence time and the coherence bandwidth for...
In this paper, we derive a performance comparison between two training-based schemes for MIMO semi-blind channel estimation. The two schemes are the conventional superimposed training scheme and the more recently proposed data-dependent superimposed pilot scheme. For both schemes, a closed-form for the outage probability and a lower bound of the bit error rate are given. We also determine for the...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.