Application of a Recurrent Neural Network to Space Deversity in SDMA and CDMA Mobile Communication Systems |
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Authors: | M Benson RA Carrasco |
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Affiliation: | (1) School of Engineering and Advanced Technology, Staffordshire University, Beaconside, Stafford, UK, GB |
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Abstract: | Linear and non-linear adaptive algorithms are investigated for Space Division Multiple Access (SDMA). SDMA is one of the emerging
techniques for multiple access of users in mobile radio, which uses spatial distribution of users for their differentiation.
The performance of the linear Square Root Kalman (SRK) algorithm for SDMA is compared to that of the non-linear Recurrent
Neural Network (RNN) technique. The proposed SDMA-RNN technique is evaluated over Rician fading channels, and it shows improved
Bit Error Rate (BER) performance in comparison with the linear SRK-based technique. The performance of SDMA-RNN is also compared
with that of Code Division Multiple Access (CDMA) systems, showing that it could be used as a viable alternative scheme for
multiple access of users. Finally, a Hybrid CDMA-SDMA system is proposed combining CDMA and SDMA-RNN systems. Hybrid CDMA-SDMA
exhibits a very good potential for increase in the capacity and the performance of mobile communications systems. |
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Keywords: | :Adaptive space diversity combining Code Division Multiple Access (CDMA) Real-time recurrent learning algorithm Recurrent neural network Space Division Multiple Access (SDMA) |
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