DISCRETE BIDIRECTIONAL ASSOCIATIVE MEMORY WITH LEARNING FUNCTION

In this paper we propose a new discrete bidirectional associative memory (DBAM) which is derived from our previous continuous linear bidirectional associative memory (LBAM). The DBAM performs bidirectionally the optimal associative mapping proposed by Kohonen. Like LBAM and NBAM proposed by one of the present authors,the present BAM ensures the guaranteed recall of all stored patterns,and possesses far higher capacity compared with other existing BAMs,and like NBAM, has the strong ability to suppress the noise occurring in the output patterns and therefore reduce largely the spurious patterns. The derivation of DBAM is given and the stability of DBAM is proved. We also derive a learning algorithm for DBAM,which has iterative form and make the network learn new patterns easily. Compared with NBAM the present BAM can be easily implemented by software.

Determining knots by optimizing the bending and stretching energies

For a given set of data points in the plane, a new method is presented for computing a parameter value（knot） for each data point. Associated with each data point, a quadratic polynomial curve passing through three adjacent consecutive data points is constructed. The curve has one degree of freedom which can be used to optimize the shape of the curve. To obtain a better shape of the curve, the degree of freedom is determined by optimizing the bending and stretching energies of the curve so that variation of the curve is as small as possible. Between each pair of adjacent data points, two local knot intervals are constructed, and the final knot interval corresponding to these two points is determined by a combination of the two local knot intervals. Experiments show that the curves constructed using the knots by the new method generally have better interpolation precision than the ones constructed using the knots by the existing local methods.

Joint association and beamforming optimization in reconfigurable intelligent surface-enhanced user-centric networks

Fully coordinated Cell-Free(CF)networks can alleviate the Inter-Cell Interference(ICI)for the cell-edge users in cellular networks.Due to the complex topology of the association between the Access Points(APs)and the users in CF networks,it is challenging to deploy CF networks in practical scenarios.In order to make CF networks feasible,we introduce User-Centric(UC)networks enabling each user served by a limited number of APs.As a low-cost and energy-efficient technology,Reconfigurable Intelligent Surface(RIS)can be embedded in UC networks to further improve the system performance.First,we provide a brief survey on the prior works in UC networks for clear comprehension.Then,we formulate a Spectral Efficiency(SE)maximization problem for RIS-enhanced UC networks.For solving the non-convex problem,we divide it into three subproblems and propose a joint optimization framework for optimizing AP-user association,active beamforming of multiple antennas at the APs,and the passive beamforming of the RIS.Besides,a channel gain based association method coupled with the design of RIS is proposed to construct a dynamic and efficient association.The subproblems about optimizing active and passive beamforming are solved with the fractional programming.Simulation results show that the proposed joint optimization framework for RIS-enhanced UC networks can obtain good SE compared with other benchmark schemes.