Software-Defined radios (SDRs) are a popular platform for developing and implementing wireless protocols. Their basic architecture consists of radio front-ends hosted on an FGPA board, and a back-end processing host for running bulk of the signal processing in software. The two components are bridged, usually by an Ethernet or PCI interface that transports the radio samples. However, nowadays, path prediction is being extensively examined for use in the context of mobile and wireless computing towards more efficient network resource management schemes. Path prediction allows the network and services to further enhance the quality of service levels that the user enjoy. The approach will be a combinational one which is based on a neural technique and wireless nodes are able to self-organize in distributed form by using only local information. The extreme adaptability to the network conditions and application level constraints makes the proposed approach well suited for different communication scenarios such as standard observation or disaster recovery. The results are expected to support the significance of the study. The system performance will be evaluated by dealing with a suite of simulation tests to show as the controlled mobility paradigm, coupled with the intrinsic re-configuring SDR capabilities of such wireless devices, allows to increase the network performances both in terms of coverage and connectivity by dynamically adapting the modulation schemes to the specific communication scenario