Energy Efficient Resource Allocation in Machine-to-Machine Communications with Multiple Access and Energy Harvesting for IoT (1711.10776v1)

Abstract: This paper studies energy efficient resource allocation for a machine-to-machine (M2M) enabled cellular network with non-linear energy harvesting, especially focusing on two different multiple access strategies, namely non-orthogonal multiple access (NOMA) and time division multiple access (TDMA). Our goal is to minimize the total energy consumption of the network via joint power control and time allocation while taking into account circuit power consumption. For both NOMA and TDMA strategies, we show that it is optimal for each machine type communication device (MTCD) to transmit with the minimum throughput, and the energy consumption of each MTCD is a convex function with respect to the allocated transmission time. Based on the derived optimal conditions for the transmission power of MTCDs, we transform the original optimization problem for NOMA to an equivalent problem which can be solved suboptimally via an iterative power control and time allocation algorithm. Through an appropriate variable transformation, we also transform the original optimization problem for TDMA to an equivalent tractable problem, which can be iteratively solved. Numerical results verify the theoretical findings and demonstrate that NOMA consumes less total energy than TDMA at low circuit power regime of MTCDs, while at high circuit power regime of MTCDs TDMA achieves better network energy efficiency than NOMA.

• The paper formulates an energy-efficient resource allocation problem for IoT M2M communications, incorporating non-linear energy harvesting and circuit power consumption.
• Optimal conditions for minimizing energy consumption are derived, and iterative algorithms are proposed for resource allocation under NOMA and TDMA strategies.
• Numerical results validate the model and show that NOMA is more energy-efficient than TDMA under low circuit power, while TDMA is superior under high circuit power.

Energy Efficient Resource Allocation in IoT M2M Communications

The paper "Energy Efficient Resource Allocation in Machine-to-Machine Communications with Multiple Access and Energy Harvesting for IoT" by Zhaohui Yang et al. thoroughly investigates resource allocation aimed at minimizing energy consumption in machine-to-machine (M2M) communications, which are integral to the Internet of Things (IoT). In exploring the domain of energy efficiency, the researchers specifically focus on non-orthogonal multiple access (NOMA) and time division multiple access (TDMA) strategies within a cellular network context where energy harvesting (EH) is a key feature.

Key Contributions

1. Problem Formulation and System Model: The paper introduces a system model for M2M communications that incorporates non-linear energy harvesting processes. By acknowledging circuit power consumption alongside transmission power, the research provides a comprehensive framework for understanding energy dynamics in IoT networks. Both NOMA and TDMA strategies are evaluated to determine their effectiveness in optimizing network energy usage.
2. Theoretical Analysis and Optimal Conditions: Through rigorous analysis, the authors derive optimal conditions under which energy consumption is minimized. Notably, it is shown that the transmission of each M2M device with the minimum throughput is optimal, and the energy consumption as a function of transmission time is convex. For NOMA, the paper produces an iterative power control and time allocation algorithm which addresses the non-convexity inherent in the optimization problem.
3. Iterative Algorithm for Resource Allocation: For both NOMA and TDMA strategies, the researchers propose algorithms that iteratively fine-tune power control and time resource allocation, effectively solving the optimization problems. These algorithms are innovatively developed to accommodate non-linear EH models, overcoming traditional linear model limitations.
4. Analytic and Numerical Validation: Extensive numerical testing corroborates the analytical findings, showing that the energy consumption model aligns with practical scenarios. Specifically, it is found that NOMA outperforms TDMA in low circuit power conditions, whereas TDMA is superior when circuit power is high.

Implications and Future Developments

These findings have significant implications for the design and management of energy-efficient IoT systems. The paper contributes robust strategies for balancing power constraints and energy harvesting capabilities, critical for sustaining long-term operations in large-scale IoT deployments. The results suggesting system-specific preferences for NOMA or TDMA based on circuit conditions provide valuable insights for network designers aiming to optimize energy efficiency.

Future developments may explore further enhancements in these algorithms to cope with increasingly complex network scenarios as IoT continues to evolve. In particular, integrating evolving communication standards and exploring multi-hop relay strategies could be worthwhile directions. The work also paves the way to further investigate how emerging technologies, such as edge computing and 5G, could be incorporated into M2M communications to enhance energy efficiency.

In conclusion, the paper makes a substantial contribution to the field of IoT by providing a nuanced analysis of energy-efficient allocation strategies in M2M communications. The methodologies and findings serve as a reference for ongoing research, continuing to push the development of sustainable IoT ecosystems.