Optimal Scheduling and Power Allocation for Two-Hop Energy Harvesting Communication Systems

Energy harvesting (EH) has recently emerged as a promising technique for green communications. To realize its potential, communication protocols need to be redesigned to combat the randomness of the harvested energy. In this paper, we investigate how to apply relaying to improve the short-term performance of EH communication systems. With an EH source and a non-EH half-duplex relay, we consider two different design objectives: 1) short-term throughput maximization; and 2) transmission completion time minimization. Both problems are joint scheduling and power allocation problems, rendered quite challenging by the half-duplex constraint at the relay. A key finding is that directional water-filling (DWF), which is the optimal power allocation algorithm for the single-hop EH system, can serve as guideline for the design of two-hop communication systems, as it not only determines the value of the optimal performance, but also forms the basis to derive optimal solutions for both design problems. Based on a relaxed energy profile along with the DWF algorithm, we derive key properties of the optimal solutions for both problems and thereafter propose efficient algorithms. Simulation results will show that both scheduling and power allocation optimizations are necessary in two-hop EH communication systems.Comment: Submitted to IEEE Transaction on Wireless Communicatio

Training Optimization for Energy Harvesting Communication Systems

Energy harvesting (EH) has recently emerged as an effective way to solve the lifetime challenge of wireless sensor networks, as it can continuously harvest energy from the environment. Unfortunately, it is challenging to guarantee a satisfactory short-term performance in EH communication systems because the harvested energy is sporadic. In this paper, we consider the channel training optimization problem in EH communication systems, i.e., how to obtain accurate channel state information to improve the communication performance. In contrast to conventional communication systems, the optimization of the training power and training period in EH communication systems is a coupled problem, which makes such optimization very challenging. We shall formulate the optimal training design problem for EH communication systems, and propose two solutions that adaptively adjust the training period and power based on either the instantaneous energy profile or the average energy harvesting rate. Numerical and simulation results will show that training optimization is important in EH communication systems. In particular, it will be shown that for short block lengths, training optimization is critical. In contrast, for long block lengths, the optimal training period is not too sensitive to the value of the block length nor to the energy profile. Therefore, a properly selected fixed training period value can be used.Comment: 6 pages, 5 figures, Globecom 201

Scalable Coordinated Beamforming for Dense Wireless Cooperative Networks

To meet the ever growing demand for both high throughput and uniform coverage in future wireless networks, dense network deployment will be ubiquitous, for which co- operation among the access points is critical. Considering the computational complexity of designing coordinated beamformers for dense networks, low-complexity and suboptimal precoding strategies are often adopted. However, it is not clear how much performance loss will be caused. To enable optimal coordinated beamforming, in this paper, we propose a framework to design a scalable beamforming algorithm based on the alternative direction method of multipliers (ADMM) method. Specifically, we first propose to apply the matrix stuffing technique to transform the original optimization problem to an equivalent ADMM-compliant problem, which is much more efficient than the widely-used modeling framework CVX. We will then propose to use the ADMM algorithm, a.k.a. the operator splitting method, to solve the transformed ADMM-compliant problem efficiently. In particular, the subproblems of the ADMM algorithm at each iteration can be solved with closed-forms and in parallel. Simulation results show that the proposed techniques can result in significant computational efficiency compared to the state- of-the-art interior-point solvers. Furthermore, the simulation results demonstrate that the optimal coordinated beamforming can significantly improve the system performance compared to sub-optimal zero forcing beamforming