BB-DCA Based Adaptive Beam Forming for Wireless Communication System

The traditional mobile radio channel has suffered always from multipath fading which invites many researchers to provide better solutions using MIMO systems. Adaptive beam forming is necessary to obtain maximum signal strength by using uplink and downlink channels. Many researchers have found different technologies to increase the performance of channel allocation. One such technology is to adapt DCA technique – Dynamic Channel Allocation in which the channels are allocated effectively by avoiding the channel interference using CCS- Cooperative Carrier Signaling technique. Also, optimization after allocating channels by defining the lower bound and upper bound in the search space using Branch and Bound technique. There are different methods of state space search available to optimise the solution. The aim of this work is to use branch and bound technique which is considered to be an effective method of finding optimal solutions by having set of feasible solutions in the search space. Multiuser MIMO system will be implemented by using this branch and bound method which is assumed to be a powerful technique among all the available existing approaches. Heuristic search is one of the efficient techniques to be applied in search space tree to find out the optimal solution among all the feasible solutions. It is designed to use MATLAB for simulating the results. This proposed Branch and Bound Dynamic Channel Allocation (BB-DCA) system using optimal search will be compared with the existing approach Channel allocation with respect to new model of channel allocation. The results of the simulation indicate that the suggested approach outperforms other current techniques.

A Generic Approach to Accelerating Belief Propagation Based Incomplete Algorithms for DCOPs via a Branch-and-Bound Technique

Belief propagation approaches, such as Max-Sum and its variants, are important methods to solve large-scale Distributed Constraint Optimization Problems (DCOPs). However, for problems with n-ary constraints, these algorithms face a huge challenge since their computational complexity scales exponentially with the number of variables a function holds. In this paper, we present a generic and easy-touse method based on a branch-and-bound technique to solve the issue, called Function Decomposing and State Pruning (FDSP). We theoretically prove that FDSP can provide monotonically non-increasing upper bounds and speed up belief propagation based incomplete DCOP algorithms without an effect on solution quality. Also, our empirically evaluation indicates that FDSP can reduce 97% of the search space at least and effectively accelerate Max-Sum, compared with the state-of-the-art.