- The paper proposes an algorithm to minimize transmission completion time in a two-user energy harvesting broadcast channel by optimizing transmit power and rates.
- A key finding identifies a cut-off power for the stronger user, influencing power allocation strategy depending on the total available power.
- The research contributes to energy-efficient communication systems and suggests extensions to more complex networks and adaptive scheduling.
Minimizing Transmission Completion Time in Energy Harvesting Broadcast Channels
This paper investigates the transmission completion time minimization in a two-user additive white Gaussian noise (AWGN) broadcast channel, leveraging a transmitter powered by energy harvesting with a rechargeable battery. The transmitter strategically optimizes its transmit power and transmission rates to efficiently deliver a fixed number of packets to two receivers. The main objective is to globally minimize the time by which all packets reach their respective destinations.
The analysis presented in this paper is rooted in determining the optimal transmission policy structure in the given broadcast channel. The authors demonstrate that the optimal total transmit power for the two-user case shares structural similarities with the single-user energy harvesting model. A crucial finding is the identification of a cut-off power level specifically for the stronger user. Depending on whether the optimal total transmit power is above or below this level, power allocation strategies differ: either all available power below this threshold is allocated to the stronger user, or any power above this level is distributed to the weaker user. This observation allows the authors to present a reduced-complexity algorithm that closely resembles solutions conceptualized for single-user scenarios.
The problem is approached by initially characterizing the maximum departure region for any given deadline and then applying these structural insights to develop an efficient algorithm aimed at minimizing the transmission completion time for a fixed packet load to both users. The duality insights leveraged show that maximizing throughput by a given deadline and minimizing time with a fixed number of bits are equivalent problems with analogous solutions.
The key methodological contribution is an iterative algorithm that reduces the broadcast channel's complexity to that of a single-user channel, utilizing proven solutions from existing literature. The algorithm accounts for different scenarios, efficiently working around the energy causality constraints inherent in energy-harvesting systems. Notably, the authors demonstrate through numerical examples the practicality and effectiveness of their proposed algorithm in reducing the transmission completion time while adhering to energy constraints.
Implications and Future Directions
The theoretical outcomes of this research significantly contribute to the burgeoning field of energy-efficient communication systems. Practically, it paves the way for the deployment of more sustainable and energy-conscious wireless networks, where the optimization of energy usage aligns with the broader goals of environmental sustainability. The paper also speculates on the feasibility of extending these methodologies to more complex networks, including systems with fading channels and multiple users.
Future work could explore adaptive online scheduling algorithms that accommodate real-time energy harvesting processes, as well as more advanced models that factor in variable battery capacities and heterogeneously distributed user power requirements. Furthermore, integrating machine learning techniques to predict energy arrivals and adaptively adjust transmission strategies could enhance the practical applicability of these findings in real-world scenarios. Such modifications would further bridge the gap toward real-time implementation in next-generation wireless communication networks.