Adaptive Resource Allocation is a prominent and necessary feature of almost all future communication systems. The transmission parameters like power, code rate and modulation scheme are adapted according to the varying channel conditions so that throughput of the OFDM system may be maximized while satisfying certain constraints like Bit Error Rate (BET) and total power at the same time. For real time systems, it is required that the adaptive process should be fast enough to synchronize with Channel State Information (CSI) and Quality of Service (QoS) demand that change rapidly. So in this paper, we have a real time system in which once CSI and QoS is fed in as input, it gives us optimal Modulation Code Pairs (MCPs) and power vectors for different subcarriers. Using a Fuzzy Rule Base System (FRBS) we obtain MCP by giving CSI and QoS and by using Differential Evolution (DE) the power vector is obtained. This becomes an example. A Gaussian Radial Basis Function Neural Network (GRBF7NN) is trained in offline mode using sufficient number of such examples. After training, given QoS and CSI as input GRBF7NN gives Optimum Power Vector (OPV) and FRBS gives optimum MCP immediately. Proposed scheme is compared with various other schemes of same domain and supremacy of the proposed scheme is shown by the simulations.
['Atta-ur-Rahman', 'Ijaz Mansoor Qureshi', 'Aqdas Naveed Malik', 'Muhammad Tahir Naseem']
A Real Time Adaptive Resource Allocation Scheme for OFDM Systems Using GRBF-Neural Networks and Fuzzy Rule Base System