Prototyping Neuroadaptive Smart Antenna for 3G Wireless Communications

Abstract

This paper describes prototyping of a neuroadaptive smart antenna beamforming algorithm using hardware-software implemented RBF neural network and FPGA system-on-programmable-chip (SoPC) approach. The aim is to implement the adaptive beamforming unit in a combination of hardware and software by estimating its performance against the fixed real-time constraint based on IMT-2000 family of 3G cellular communication standards

FPGA Prototyping of RNN Decoder for Convolutional Codes

“…The amount of logic could be reduced considerably if a fewer number of neurons are implemented and a sequential neuron update strategy is adopted where a smaller number of neurons are time-division-multiplexed across the network. In fact, simulation results show [16] that the sequential update variation offers improved decoding performance over the fully parallel decoding strategy.…”

“…In [13,15] another method of reducing the circuitry necessary for multiplication is proposed which is based on bit-serial stochastic computing techniques. A successful prototyping of a neuro-adaptive smart antenna beam-forming algorithm using combined hardware-software implemented radial basis function (RBF) neural network has been reported in [16].…”

Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

“…Reconfigurable hardware platforms have a balance between hardware and software solutions exclusively, as they have the programmability of software with performance capacity approaching that of a custom hardware implementation [22]. Because of several attractive features like low cost as compared to MPLDs (mask programmable logic devices), easy implementation and reprogrammability, the FPGAs are being widely used for creating reconfigurable hardware of neural networks for different applications [23][24][25][26][27] but the literature on this approach for microwave applications domain is limited [28][29][30][31]. To et al [28] have described prototyping of a neuroadaptive smart antenna beam-forming algorithm using hardware-software approach by implementing the RBF neural network on FPGA platform.…”

“…Because of several attractive features like low cost as compared to MPLDs (mask programmable logic devices), easy implementation and reprogrammability, the FPGAs are being widely used for creating reconfigurable hardware of neural networks for different applications [23][24][25][26][27] but the literature on this approach for microwave applications domain is limited [28][29][30][31]. To et al [28] have described prototyping of a neuroadaptive smart antenna beam-forming algorithm using hardware-software approach by implementing the RBF neural network on FPGA platform. For prototyping strategy, they have used three steps, implementing a simulation model in MATLAB software, translating it into generic C/C++ working model with an external matrix arithmetic library, and, finally, implementing this C/C++ model on the Altera "APEX FPGA EP20K200E" embedded processor platform [28].…”

“…To et al [28] have described prototyping of a neuroadaptive smart antenna beam-forming algorithm using hardware-software approach by implementing the RBF neural network on FPGA platform. For prototyping strategy, they have used three steps, implementing a simulation model in MATLAB software, translating it into generic C/C++ working model with an external matrix arithmetic library, and, finally, implementing this C/C++ model on the Altera "APEX FPGA EP20K200E" embedded processor platform [28]. Al Zuraiqi et al [29] have designed a neural network FPGA controller for reconfigurable antennas and the optimized weights, biases, and network topology have been configured on Xilinx's FPGA.…”