Enhancing the spectral efficiency and energy efficiency of underwater channel communication by optimal cooperative spectrum sensing using hybrid optimization

Abstract

Summary Underwater wireless sensor networks (UWSNs) contain quite a lot of components such as vehicles and sensors that are deployed in a specific acoustic area to perform collaborative monitoring and data collection errands. These networks are adopted interactively between diverse nodes and ground‐based stations. Currently, UWSNs face problems and challenges that pertain to limited bandwidth, media access control, high propagation delay, 3D topology, spectrum sensing, resource utilization, routing, and power constraints. This proposal deals with the intelligent spectrum sensing in underwater cognitive sonar communication networks (CSCN). Here, the improved performance of spectrum sensing in underwater communication is attained by optimizing the cooperative spectrum sensing and data transmission. The parameters of system like subchannel allocation and transmission power is optimized by a new hybrid meta‐heuristic algorithm by integrating the concepts of deer hunting optimization algorithm (DHOA) and lion algorithm (LA) termed as lion‐enabled DHOA (L‐DHOA). The main intention of optimizing these parameters is to maximize the spectrum efficiency (SE) and energy efficiency (EE) of the underwater channel communication system. From the analysis, with respect to convergence rate, minimum detection probability, and local sensing time, it is proved that the novel hybrid optimization algorithm keeps a great role in making the trade‐off between the SE and EE in underwater channel modeling.

Enhanced capsule generative adversarial network for spectrum and energy efficiency of cooperative spectrum prediction framework in cognitive radio network

“…Spectrum efficiency able to use unused primary user spectrum, when energy efficiency is determined as spectrum efficiency ratio to the overall power consumption through SUs. 2,3 The energy efficiency is a significant factor for battery-powered devices as well as deeming environmental conditions. Spectrum sensing is a main process of cognitive radio networks.…”

“…The performance of cognitive radio networks (CRNs) are assessed under spectrum efficiency with energy efficiency. Spectrum efficiency able to use unused primary user spectrum, when energy efficiency is determined as spectrum efficiency ratio to the overall power consumption through SUs 2,3 . The energy efficiency is a significant factor for battery‐powered devices as well as deeming environmental conditions.…”

Spectral and energy efficiency tradeoff in optical code division multiple access networks

“…In a communication system, the EE and SE are often conflicting metrics, that is, increasing one parameter reduces another and vice versa 4‐10 . Moreover, in the CDMA‐based systems, the use of sophisticated detection techniques has a positive impact on the EE‐SE tradeoff, since advanced detectors are able to reduce the effects of multiple access interference (MAI), so that the power levels necessary to achieve quality of service (QoS) can be reduced substantially, resulting in more energy efficient systems.…”