Training-Based MIMO Systems—Part I: Performance Comparison

Abstract

In this paper, we make a performance comparison between two different training-based schemes for multiple-input multiple-output (MIMO) channel estimation. The two schemes are the conventional time-multiplexed pilot (CP) scheme and the more recently suggested superimposed pilot (SIP) scheme. Unlike previous comparisons found in the literature, which are mostly based on estimation error performances, the performance comparison in this paper is made by deriving and comparing the maximum data rate (or rather a tight lower bound on the maximum mutual information) achieved by each scheme. By using the maximum mutual information criterion, for each training scheme, we can optimally allocate the time and power spent on transmission of training and data sequences. Once the system parameters (time and power) are tuned to give optimal performance, we can compare their respective maximum data rate. The theory is applied to a blockwise flat-fading MIMO channel, and it is found that in certain scenarios (such as many receive antennas and/or short channel coherence times), it is beneficial to use the SIP. In other scenarios, the SIP scheme suffers from a higher estimation error, and its gain over the CP scheme is often lost.

Low-mobility channel tracking for MIMO–OFDM communication systems

“…Moreover, we extend the analysis conducted regarding Kalman channel tracking of SISO-OFDM systems in [25] to MIMO-OFDM systems. In the process, we extend the discussion in [18,21] by considering a "slowly" time-varying frequency-selective channel. In other words, while both [18,21] consider a block-invariant frequency-flat channel, we consider a frequency-selective channel that is correlated over successive symbol blocks.…”

“…Remark: By enforcing the orthogonality condition and by choosing MIMO-OFDM with FDM training symbols as the affine precoder scheme, we have broken down (8) into (20) and (21). As a result, the impact of overlapping data-bearing symbols on the channel estimator has been http://asp.eurasipjournals.com/content/2013/1/78 circumvented.…”

“…One, when the receiver obtains joint Bayesian channel and symbol estimates and two, when the receiver computes channel estimates followed by their utilization in obtaining symbol estimates. The model presented in [18] was generalized in [21] by considering a superimposed training scheme of which time multiplexed training can be termed as a special case. Based on the mutual information bounds derived, a comparison between the superimposed training and the conventional time multiplexed training is performed by optimizing over training design, number of transmit antennas, and a training/information symbol power budget.…”

Design of Learning-Based MIMO Cognitive Radio Systems

“…5 The similar approach has also been suggested in [16], [21], [22] from different viewpoints. Hence, the optimal training design is found from the problem…”

Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems

“…This kind of technology can have a very high impact, but it usually requires long observation time and high complexity. Training based super imposed RS on data signals, which locates RS stream and data stream in the same resources, were also introduced in [8,9]. This can give very efficient results for reducing RS overhead because data can be transmitted in all time and frequency resources.…”