Current centralized restoration schemes are unsuitable for the increase of scale and complexity of networks. A novel distributed network partition scheme is proposed in this paper. In this scheme, a large-scale network can be partitioned into some wheellike sub-networks with nuclear nodes. In wheellike sub-networks, ring links and spoke links could provide reciprocal safeguard. Based on such structure, different distributed restoration schemes can be combined for failure restoration. The proposed partition approach has been implemented through computer simulation, and it was tested on practical national-scale optical networks. The simulation result shows that this scheme is practicable and effectual.

A low-complexity signal detection approach based on the Kaczmarz algorithm (KA) is proposed to iteratively realize minimum mean square error (MMSE) detection for uplink massive multiple-input multiple-output (MIMO) systems. While KA is used for straightforward matrix inversion, the MMSE detection requires the computation of the Gram matrix with high complexity. In order to avoid the Gram matrix computation, an equivalent augmented matrix is applied to KA-based MMSE detection. Moreover, promising initial estimation and an approximate method to compute soft-output information are utilized to further accelerate the convergence rate and reduce the complexity. Simulation results demonstrate that the proposed approach outperforms the recently proposed Neumann series, conjugate gradient, and Gauss-Seidel methods in complexity and error-rate performance. Meanwhile, the FPGA implementation results confirm that our proposed method can efficiently compute the approximate inverse with low complexity.

Co-channel interference (CCI) is becoming a challenging factor that causes performance degradation in modern communication systems. The receiver equipped with multiple antennas can suppress such interference by exploiting spatial correlation. However, it is difficult to estimate the spatial covariance matrix (SCM) of CCI accurately with limited number of known symbols. To address this problem, this paper first proposes an improved SCM estimation method by shrinking the variance of eigenvalues. In addition, based on breadth-first tree search schemes and improved channel updating, a low complexity iterative detector is presented with channel preprocessing, which not only considers the existence of CCI but also reduces the computational complexity in terms of visited nodes in a search tree. Furthermore, by scaling the extrinsic soft information which is fed back to the input of detector, the detection performance loss due to max-log approximation is compensated. Simulation results show that the proposed iterative receiver provides improved signal to interference ratio (SIR) gain with low complexity, which demonstrate the proposed scheme is attractive in practical implementation.