Ambient RF energy harvesting in ultra-dense small cell networks: performance and trade-offs

Abstract

Abstract-In order to minimize electric grid power consumption, energy harvesting from ambient RF sources is considered as a promising technique for wireless charging of low-power devices. To illustrate the design considerations of RF-based ambient energy harvesting networks, this article first points out the primary challenges of implementing and operating such networks, including non-deterministic energy arrival patterns, energy harvesting mode selection, energy-aware cooperation among base stations (BSs), etc. A brief overview of the recent advancements and a summary of their shortcomings are then provided to highlight existing research gaps and possible future research directions. To this end, we investigate the feasibility of implementing RF-based ambient energy harvesting in ultradense small cell networks (SCNs) and examine the related tradeoffs in terms of the energy efficiency and signal-to-interferenceplus-noise ratio (SINR) outage probability of a typical user in the downlink. Numerical results demonstrate the significance of deploying a mixture of on-grid small base stations (SBSs) (powered by electric grid) and off-grid SBSs (powered by energy harvesting) and optimizing their corresponding proportions as a function of the intensity of active SBSs in the network.

NOMA Throughput and Energy Efficiency in Energy Harvesting Enabled Networks

“…In summary, at the κth iteration we solve the following convex optimization problem of computational complexity (49) Algorithm 3 outlines the steps to solve the "transmit TSbased" algorithm for throughput max-min optimization problem (42). Like Algorithm 1, it converges at least to a local optimal solution satisfying the KKT condition.…”

“…till reaching a value more than or equal to 0, for a fixed ρ (0) . If problem (50) is infeasible with ρ (0) or solving (50) fails to give a positive optimal value, we repeat the above process for a different value of ρ (0) in order to find a feasible point w E,(0) , w I,(0) , R (0) , ρ (0) for (42). 3 Next, we address the following energy-efficiency maximization problem 2 .…”

“…In contrast to the objective function in (14), the power consumption function in the denominator of the objective function in (51) is no longer convex, making equivalently transformed problem like (14) no longer useful. Nevertheless, we now also develop another iterative procedure, where (51) is seen no more computationally difficult than the throughput optimization problem (42).…”

Prioritizing Data/Energy Thresholding-Based Antenna Switching for SWIPT-Enabled Secondary Receiver in Cognitive Radio Networks

“…, N s − 2, and I N s −1 (ξ s , γ th ) were shown in Appendix B to be expressed as in (76), (78), and (81). Consequently, the secondary data outage probability is given by (16), where f…”

“…In [15], the distributed uplink user association problem in EH ultra-dense small cell networks (UD-SCNs) was solved using an approach based on the meanfield multi-armed bandit games. In [16], the ambient RF-based EH (UD-SCNs) was considered where the implementation feasibility and the tradeoffs in terms of the energy efficiency and data outage probability of a typical user were investigated. In [17], using the online ski rental framework, the on/off scheduling problem of self-powered small cell base stations (SBSs) was considered which aims to minimize the operational energy consumption and transmission delay costs.…”