Study On MIMO Antenna Selection Technology Based On Capacity Performance

Along with the development of wereless communication, the demand of high speeddata communication is increasing, conventional single antenna communication systemcapacity can not meet the requirement of practial application, and the communicationsystem reliability has yet to be further improved. Multiple-Input Multiple-Output (MIMO)technology can utilize multipath effect, and improve the transmission rate andcommunication quality. It becomes one of key technologies for new generation wirelesscommunication systems. However, MIMO system needs multiple radio frequency (RF)chains for employing multiple antennas, this increase the cost of additional hardware andthe complexity of signal processing substantially, and limits development and generalizationof MIMO technology to a great extent. Antenna selection technology is to use only anantenna subset of transmit and receive with better performance, it can reduce the expense ofhardware and complexity of processing with less performance loss, becomes research focusof MIMO wereless communication field.As existing antenna selection algorithms in MIMO systems, the exhaustive searchalgorithm gets the optimum performance, but it requires lots of matrix operations, so leadsto very high complexity. Some sub-optimal antenna selection algorithms emerge in order todecrease the complexity of algorithm, but most of these algorithms result in a greaterperformance loss or still higher complexity, and mainly concentrate on the antenna selectionresearch in the non-correlated channels.According to the problem, this paper takes the capacity performance maximize for atarget, proposes an antenna selection algorithm based on dissimilarity, an improvedselection algorithm based on CSA for correlated channel, and discussed several jointtransmit/receive antenna selection technology based on smart algorithm. The main resultsare as follows:1. MIMO system model in complex Gaussian random channel and correlated channelis introduced, then an analysis of MIMO system capacity and the impact of the channelcorrelation is given, several classical algorithms capacity performance are compared, thesimulation results proved exhaustive search algorithm can obtain the optimal capacity, butthe complexity is very high, the decremental algorithm and incremental algorithm get theclosed optimal capacity, and lower the system complexity at the meantime. 2. The paper proposes an antenna selection algorithm based on dissimilarity, thealgorithm goal is to maximize capacity. It analyzes the impact of relative error and absoluteerror to the property of dissimilarity algorithm; meanwhile the complexity is computed andanalyzed to prove that the proposed algorithm maintains lower computation complexity. Thecapacity performance of the proposed algorithm, exhaustive search, the algorithms based oncorrelation and similarity are compared, the results of simulation proved proposed algorithmoffers an improved channel capacity.3. For correlated channel, an improved antenna selection algorithm based on CSA ispresented, which selects the antenna subset with the lowest average correlation and thelargest determinant of correlation matrix. With less antennas selected, the proposedalgorithm decreases the computational complexity, obtains almost the same property withcorrelated selection algorithm, and is very close to the optimal selection algorithm.Simulation results demonstrate the effectiveness and reliability of the proposed algorithm.4. Joint transmit/receive antenna selection algorithm based on priority-based geneticand simulated annealing algorithm are described, and getting a new algorithm throughcombining them. Then we discussed the principle and property of these algorithms andsome experimental results is given.