MIMO capacity through correlated channels in the presence of correlated interferers and noise: A (not so) large N analysis
The use of multiple-antenna arrays in both transmission and reception promises huge increases in the throughput of wireless communication systems. It is therefore important to analyze the capacities of such systems in realistic situations, which may include spatially correlated channels and correlat...
| Главные авторы: | , , |
|---|---|
| Формат: | Conference item |
| Опубликовано: |
2003
|
| _version_ | 1826303952777379840 |
|---|---|
| author | Moustakas, A Simon, S Sengupta, A |
| author_facet | Moustakas, A Simon, S Sengupta, A |
| author_sort | Moustakas, A |
| collection | OXFORD |
| description | The use of multiple-antenna arrays in both transmission and reception promises huge increases in the throughput of wireless communication systems. It is therefore important to analyze the capacities of such systems in realistic situations, which may include spatially correlated channels and correlated noise, as well as correlated interferers with known channel at the receiver. Here, we present an approach that provides analytic expressions for the statistics, i.e., the moments of the distribution, of the mutual information of multiple-antenna systems with arbitrary correlations, interferers, and noise. We assume that the channels of the signal and the interference are Gaussian with arbitrary covariance. Although this method is valid formally for large antenna numbers, it produces extremely accurate results even for arrays with as few as two or three antennas. We also develop a method to analytically optimize over the input signal covariance, which enables us to calculate analytic capacities when the transmitter has knowledge of the statistics of the channel (i.e., the channel covariance). In many cases of interest, this capacity is very close to the full closed-loop capacity, in which the transmitter has instantaneous channel knowledge. We apply this analytic approach to a number of examples and we compare our results with simulations to establish the validity of this approach. This method provides a simple tool to analyze the statistics of throughput for arrays of any size. The emphasis of this paper is on elucidating the novel mathematical methods used. |
| first_indexed | 2024-03-07T06:10:29Z |
| format | Conference item |
| id | oxford-uuid:ef5198ea-582f-40f4-9bc6-7eb9cd55a0ba |
| institution | University of Oxford |
| last_indexed | 2024-03-07T06:10:29Z |
| publishDate | 2003 |
| record_format | dspace |
| spelling | oxford-uuid:ef5198ea-582f-40f4-9bc6-7eb9cd55a0ba2022-03-27T11:39:18ZMIMO capacity through correlated channels in the presence of correlated interferers and noise: A (not so) large N analysisConference itemhttp://purl.org/coar/resource_type/c_5794uuid:ef5198ea-582f-40f4-9bc6-7eb9cd55a0baSymplectic Elements at Oxford2003Moustakas, ASimon, SSengupta, AThe use of multiple-antenna arrays in both transmission and reception promises huge increases in the throughput of wireless communication systems. It is therefore important to analyze the capacities of such systems in realistic situations, which may include spatially correlated channels and correlated noise, as well as correlated interferers with known channel at the receiver. Here, we present an approach that provides analytic expressions for the statistics, i.e., the moments of the distribution, of the mutual information of multiple-antenna systems with arbitrary correlations, interferers, and noise. We assume that the channels of the signal and the interference are Gaussian with arbitrary covariance. Although this method is valid formally for large antenna numbers, it produces extremely accurate results even for arrays with as few as two or three antennas. We also develop a method to analytically optimize over the input signal covariance, which enables us to calculate analytic capacities when the transmitter has knowledge of the statistics of the channel (i.e., the channel covariance). In many cases of interest, this capacity is very close to the full closed-loop capacity, in which the transmitter has instantaneous channel knowledge. We apply this analytic approach to a number of examples and we compare our results with simulations to establish the validity of this approach. This method provides a simple tool to analyze the statistics of throughput for arrays of any size. The emphasis of this paper is on elucidating the novel mathematical methods used. |
| spellingShingle | Moustakas, A Simon, S Sengupta, A MIMO capacity through correlated channels in the presence of correlated interferers and noise: A (not so) large N analysis |
| title | MIMO capacity through correlated channels in the presence of correlated interferers and noise: A (not so) large N analysis |
| title_full | MIMO capacity through correlated channels in the presence of correlated interferers and noise: A (not so) large N analysis |
| title_fullStr | MIMO capacity through correlated channels in the presence of correlated interferers and noise: A (not so) large N analysis |
| title_full_unstemmed | MIMO capacity through correlated channels in the presence of correlated interferers and noise: A (not so) large N analysis |
| title_short | MIMO capacity through correlated channels in the presence of correlated interferers and noise: A (not so) large N analysis |
| title_sort | mimo capacity through correlated channels in the presence of correlated interferers and noise a not so large n analysis |
| work_keys_str_mv | AT moustakasa mimocapacitythroughcorrelatedchannelsinthepresenceofcorrelatedinterferersandnoiseanotsolargenanalysis AT simons mimocapacitythroughcorrelatedchannelsinthepresenceofcorrelatedinterferersandnoiseanotsolargenanalysis AT senguptaa mimocapacitythroughcorrelatedchannelsinthepresenceofcorrelatedinterferersandnoiseanotsolargenanalysis |