Intelligent multichannel cross-layer framework for enhanced energy-efficiency in 6G-IoT wireless networks

Abstract

The sixth-generation (6G) communication technology is expected to enable a fully-connected world with enhanced wireless connectivity, accessibility, reliability, and performance. 6G systems will be critical in addressing the massive-connectivity requirements for Internet-of-Things (IoT) networks. Cognitive radio (CR) technology is considered a promising solution in 6G networks that can provide tremendous spectrum opportunities to support large-scale IoT deployments. Due to the IoT’s energy-constrained and massively connected nature, existing CR-based communication protocols cannot be directly implemented in 6G-IoT networks. This is because they are usually designed to enhance spectrum-utilization in networks with finite users. Therefore, energy-efficient protocols are required to support the massive-connectivity requirement for energy-constrained IoT nodes. Non-persistent Carrier-Sense-Multiple-Access (CSMA)-based protocols are recognized as energy-effective spectrum-access paradigms for networks with massive users. This paper proposes a novel energy-efficient multi-channel cross-layer MAC framework that utilizes a non-persistent CSMA protocol in CR-enabled 6G-IoT networks. Based on a joint adaptation of the physical and MAC layer parameters, our framework aims to improve IoT networks’ energy efficiency (EE). Specifically, we formulate and solve the per-bit EE maximization problem subject to several design constraints. Numerical results show that our multi-channel cross-layer framework enhances network EE by up to 50% compared to a reference single-channel cross-layer design.