Application of a recurrent neural network to space diversity in SDMA and CDMA mobile communication systems

作者：Benson M; Carrasco RA^*

来源：Neural Computing & Applications, 2001, 10(2): 136-147.

DOI：10.1007/s005210170005

• adaptive space diversity combining
• Code Division Multiple Access (CDMA)
• real-time recurrent learning algorithm
• recurrent neural network
• Space Division Multiple Access (SDMA)

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 linens 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 e in comparison with the linear SRK-based technique. The performance of SDMA-RNN is also compared with that of Code Division Multiple Acc ess (CDMA) systems, showing that it could he 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.

• 出版日期2001