Name
Abstract | ||
|---|---|---|
It is now well understood that by exploiting the available additional spatial dimensions, multiple-input multiple-output (MIMO) communication systems provide capacity gains, compared to a single-input single-output systems without increasing the overall transmit power or requiring additional bandwidth. However, these large capacity gains are feasible only when the perfect knowledge of the channel is available to the receiver. Consequently, when the channel knowledge is imperfect, as is common in practical settings, the impact of the achievable capacity needs to be evaluated. In this study, we begin with a general MIMO framework at the outset and specialize it to the case of orthogonal frequency division multiplexing (OFDM) systems by decoupling channel estimation from data detection. Cyclic-prefixed OFDM systems have attracted widespread interest due to several appealing characteristics not least of which is the fact that a single-tap frequency-domain equalizer per subcarrier is sufficient due to the circulant structure of the resulting channel matrix. We consider a low-mobility wireless channel which exhibits inter-block channel variations and apply Kalman tracking when MIMO–OFDM communication is performed. Furthermore, we consider the signal transmission to contain a stream of training and information symbols followed by information symbols alone. By relying on predicted channel states when training symbols are absent, we aim to understand how the improvements in channel capacity are affected by imperfect channel knowledge. We show that the Kalman recursion procedure can be simplified by the optimal minimum mean square error training design. Using the simplified recursion, we derive capacity upper and lower bounds to evaluate the performance of the system. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1186/1687-6180-2013-78 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
MIMO-OFDM, Cramér-Rao lower bound, Optimal training design, Channel estimation, Kalman filtering, Capacity bounds | Computer science,Electronic engineering,Artificial intelligence,Channel capacity,MIMO-OFDM,MIMO,Communication channel,Minimum mean square error,Bandwidth (signal processing),Electrical engineering,Orthogonal frequency-division multiplexing,Machine learning,Precoding | Journal |
Volume | Issue | ISSN |
2013 | 1 | 1687-6180 |
Citations | PageRank | References |
2 | 0.34 | 33 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Srikanth Pagadarai | 1 | 81 | 4.96 |
| Alexander M. Wyglinski | 2 | 375 | 48.05 |
| Christopher Anderson | 3 | 2 | 0.34 |