How to avoid multipath fading in LTE

The steadily growing demand for higher bandwidth efficiency, range and reliability as well as higher transmission rates in the third generation (3G) and in future generations of wireless communication systems has led to intensive research in the field of multi-antenna communication. Furthermore, recently Orthogonal Frequency-Division Multiplexing (OFDM) emerged as an advantageous candidate for future mobile radio systems. reason advantageous properties of ODFM, such as efficient use of the bandwidth, channel equalization, and robustness against multipath propagation. Because of these facts are Multiple-Input-Multiple-Output (MIMO) systems in connection with ODFM are very promising processes that have already been included in many new mobile radio standards such as Long Term Evolution (LTE) and Worldwide interoperability for Microwave Access (WiMAX).

Space-Time Coding (STC) methods are able to take advantage of the spatial diversity that multi-antenna systems bring with them. STC procedures were also used with MIMO-OFDM cellular systems combined as opposed to the reliability and the transfer rate Increase single antenna systems. In particular, the so-called Orthogonal Space-Time Block Codes (OSTBCs) a popular one Class of STC procedures. They are known for not just the to maximize spatial diversity gain, but also simple To allow decoding methods. Enjoy the benefits of the theory promised advantages of orthogonally coded MIMO-OFDM systems but only if accurate channel status information (CSI) is on Recipients can be assumed. A lack of CSI at the receiver goes with considerable losses in the performance of the MIMO-OFDM systems hand in hand.

In practice, the knowledge of the transmission channel usually obtained with the aid of transmitted pilot symbols, which at the expense of a reduced bandwidth efficiency and higher power consumption of the Message transmission are attached. Blind channel estimation methods are of particular interest because they use the Avoid the disadvantages mentioned above.

The focus of this work is on the development of algorithms for blind channel estimation for orthogonally coded MIMO and MIMO-OFDM systems.

First we introduce a new model for orthogonally coded Single-carrier MIMO systems. Based on this model, we will prove one special subspace property of the vectorized transfer function of the channel for single-carrier systems. We thus justify a blind channel estimation method analytically closed representation, which relates directly to the individual Can use subcarriers of a MIMO-OFDM system. We also beat two Method for resolving ambiguities in channel estimation let avoid.

Next we generalize the special subspace property of the vectorized transfer function for single-carrier systems on multi-carrier systems and propose for orthogonal space-time-coded MIMO-OFDM systems use a blind channel estimator before, which has an analytically closed representation. We also manage Conditions under which a clear channel estimation is possible.

Then we will help develop a new type of algorithm for MIMO-OFDM systems OSTBCs based on semi-definite relaxation (SDR). We show that the non-convex channel estimation problem turns out to be a convex semi-definite program (SDP) can be approximated. This can do that Channel estimation problem can be solved with the modern methods of convex optimization.

Finally, we develop algorithms with analytically closed Representation for blind channel estimation based on the relaxed maximum likelihood recipient and the Capon receiver. Compared Both of them point to the algorithm based on SDR technology Algorithms make a different tradeoff between performance and Complexity on.

Assuming a time spread of the radio channel below the Duration of an OSTBC-OFDM symbol it is possible in the time domain to combine the parameters of all subcarriers estimate. This facilitates coherent data processing across all Subcarriers compared to traditional estimation methods in which the Subcarrier processed separately from each other. The proposed channel estimation methods not only offer a significantly reduced computational effort, but also improve the accuracy of the estimate.