Performance Analyses of Adaptive Handover Decision Algorithm using Spectrum Aggregation in Long Term Evolution - Advanced Network

The ever increasing demand for wireless broadband services has imposed severe bandwidth constraints on wireless communication system. These demands have led to the technological responses such as the advancement in radio access, coding and modulation schemes as well as spectrum(carrier) aggregation and cognitive radio capabilities of Long Term Evolution - Advanced (LTE-A) and Fifth Generation (5G) networks developed by the Third Generation Partnership Project (3GPP). The aspect of spectrum or carrier aggregation uses cognitive radio technology that implements the dynamic spectrum access for spectrum utilization by sensing and using the underutilized spectrum on co-primary basis without interference to the primary users. In this work, LTE-A network building and deployment as well as configuration management through radio resources allocation and system level performance evaluation for the Proposed TVWS, MIF and CONV handover decision algorithms were successfully carried out. The deployed network comprised of 61 eNBs (cell) layout with 500m inter-site-distance. Three eNBs at some arbitrary chosen locations were configured to operate in TVWS. Each cell has three sectors and each sector was configured with two component carriers; CC1 of 2 and CC2 with 2.0203 or 2.6 or 3.5 GHz for licensed LTE-A depending on the aggregation scenario. On the other hand, the TVWS eNBs have two component carriers (CC) with CC1 having frequency of 2 GHz while the other CC2 would have any one of frequencies from 474 MHz to 862 MHz based on the free channel obtained through the energy based channel sensing process. The proposed TVWS algorithm showed average low RLF rate values than the Conventional (CONV) and Multi-Influence Factors Handover Decision Algorithms (MIF-HODA). This implied that the network operating under the proposed TVWS algorithm presented low radio link decoding errors when compared with the validation algorithms MIF and CONV. This presented remarkable channel quality and overall LTE-A network performance improvements.