| dc.description.abstract | Modern technology is growing at an explosive rate, that developed such as AI(artificial intelligence),5G network(fifth Generation Mobile Network),etc. The data throughput of these technologies is a bit higher, MIMO systems have been gradually unable to load. Therefore, the number of antennas is increased to form a Massive MIMO system. Common antenna numbers are 64x8、128x8 and so on. However, this paper tries to implement it with the more difficult number of antennas which is 128x32. When calculating the MMSE (Minimum Mean Square Error), the complexity of the inverse matrix in the algorithm is O(N_t^3). Therefore, this paper uses the Joint Conjugate Gradient Jacobi algorithm to approximate the inverse matrix in MMSE. This algorithm uses conjugate gradient method to iterate before and ends with the Jacobi method. Because the CG method can provide better convergence, so use it to search the correct direction first. Later, iterate with the less complex Jacobi method. The Jacobi method is an iterative algorithm through the diagonal matrix and residual matrix. Because Massive MIMO has diagonal advantage, the iteration of the Jacobi method can also reduce the BER(Bit Error Rate) of the system.
In the circuit architecture, in order to operate the Gram matrix, this paper divides the channel matrix into quarters and use the conjugation and symmetry of the Gram matrix for parallel processing and finally sum to obtain higher throughput. The overall circuit architecture is designed with pipeline and divided into three stages for operation. The chip design is implemented in TSMC 40 nm CMOS technology. The core area is 3.72 mm^2,maximum frequency is 423MHz, dynamic power consumption is 877mW, throughput can achieve 768Mbps. | en_US |