Energy Efficient Machine-Type Communications over Cellular Networks A Battery Lifetime-Aware Cellular Network Design Framework
Sammanfattning: Internet of Things (IoT) refers to the interconnection of uniquely identifiable smart devices which enables them to participate more actively in everyday life. Among large-scale applications, cheap and widely spread machine-to-machine (M2M) communications supported by cellular networks, also known as machine-type communications (MTC), will be one of the most important enablers for the success of IoT. As the existing cellular infrastructure has been optimized for a small number of long-lived communications sessions, serving a massive number of machine-type devices with extremely diverse quality of service requirements is a big challenge. Also, most machine nodes are battery-driven, and hence, long battery life is crucial for them especially when deployed in remote areas.The present work is devoted to energy consumption modeling, battery lifetime analysis, and lifetime-aware network design for massive MTC over cellular networks. We first develop a realistic energy consumption model for MTC, based on which, network battery lifetime models are defined. To address the massive concurrent access issue and save energy in data transmission, we first consider cluster-based MTC and let machine devices organize themselves locally, create machine clusters, and communicate through the cluster-heads to the base-station (BS). Toward this end, we need to find where clustering is feasible, and how the clusters must be formed. These research problems as well as some other aspects of cluster-based MTC are investigated in this work, battery lifetime-aware solutions are derived, and performance evaluations for the proposed solutions are provided.For direct communications of the unclustered nodes and cluster-heads with the BS, we investigate the potential benefit in lifetime-aware uplink scheduling and transmit power control. Analytic expressions are derived to demonstrate the impact of scheduling on the individual and network battery lifetimes. The derived expressions are subsequently employed in uplink scheduling and transmit power control for mixed-priority MTC traffic in order to maximize the network lifetime. Besides the main solutions, low-complexity solutions with limited feedback requirement are also investigated.Finally, we investigate the impact of energy saving for the access network on battery lifetime of machine-type devices. We present a queuing system to model the uplink transmission of a green base station which serves two types of distinct traffics with strict requirements on delay and battery lifetime. Then, the energy-lifetime and energy-delay tradeoffs are introduced, and closed-form expressions for energy consumption of the base station, average experienced delay in data transmission, and expected battery lifetime of machine devices are derived. Numerical results show the impact of energy saving for the access network on the introduced tradeoffs, and figure out how to trade the tolerable lifetime/delay of the users for energy saving in the access network.The derived solutions are finally extended to the existing LTE networks, and simulation results in the context of LTE are presented. The simulation results show that the proposed solutions can provide substantial network lifetime improvement and network maintenance cost reduction in comparison with the existing approaches.
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