Convergence of WSN and cognitive cellular network using maximum frequency reuse

Abstract

The exponential increase in wireless sensor network (WSN) applications and devices crave for the communication resources. Regardless of the access mechanism, the unlicensed industrial, scientific and medical spectrum band is insufficient for massive simultaneous connections. On the other hand, the 4G and the emerging 5G cellular networks provide high data rates of 75–300 Mbps and 7.5 Gbps, respectively. Nevertheless, WSN's extempore inclusion in the cellular network can heavily congest its Random Access Channel (RACH). In this study, the authors investigate a unique concept of maximum frequency reuse in small evolved NodeBs, and subsequently, recommend their deployment in WSN congested areas. They also propose a cognitive radio (CR) algorithm to offload SeNB load over Macrocell. Authors formulate proposed SeNB-based cellular network in an analytical RACH model, where dynamics of SeNB's and Macrocell users are scrutinised. Observations show that the proposed scheme allocates more users with low collision probability. Simulation results show that the proposed scheme imparts a significant increase of 1–3 Mbps in downlink throughput and culminate a lift of 2–9 Mbps in uplink throughput. The average uplink signal-to-interference-plus-noise-ratio (SINR) is also proliferated by approximately 10–15 dB. Moreover, CR-based SeNB's smart offloading outperforms the conventional offloading in terms of downlink and uplink SINR by about 1–3 dB.