Opportunistic CR-NOMA Transmissions for Zero-Energy Devices : A DRL-driven Optimization Strategy

• To efficiently accommodate RF energy-harvesting (EH) capable device in a wireless network with prescheduled devices, this letter designs the deep reinforcement learning (DRL)-driven energy-efficient transmission strategy. The transmission strategy handles the EH-device uplink transmissions opportunistically using cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) scheme while maximizing energy efficiency (EE). In this respect, firstly, we formulate the EE optimization problem of the EH-device while considering its RF circuit power consumption. Secondly, we divide the original, non-convex problem into a two-layer optimization problem, and solve it sequentially as i) we theoretically derive the optimal transmit power and time-sharing coefficient parameters from the first layer, and ii) using the derived parameters in the second layer, we solve the one-dimensional continuous space optimization problem through a DRL technique, recognized as a combined experience replay deep deterministic policy gradient (CER-DDPG). Finally, the numerical results show that, under different operational scenarios, the proposed approach outperforms benchmark DDPG and Stochastic algorithms in terms of EE. 

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)

Keyword

deep deterministic policy gradient (DDPG)

energy efficiency (EE)

Energy harvesting

NOMA

non-orthogonal multiple access (NOMA)

Optimization

Power demand

Quality of service

Radio frequency

RF energy harvesting (EH)

Wireless networks

Zero-energy radio

Publication and Content Type

ref (subject category)

art (subject category)