eICIC Configuration of Downlink and Uplink Decoupling With SWIPT in 5G Dense IoT HetNets

Abstract

Interference management and power transfer can provide a significant improvement over the 5th generation mobile networks (5G) dense Internet of Things (IoT) heterogeneous networks (HetNets). In this paper, we present a novel approach to simultaneously manage inferences at the downlink (DL) and uplink (UL), and to identify opportunities for power transfer and additional UL transmissions integrated with existing protocols and infrastructures for enhanced inter-cell interference coordination (eICIC) protocol in dense IoT HetNets, while considering practical non-linear energy harvesting (EH) model. The design is formulated as the joint optimization of interference aware UL/DL decoupling, airtime resource allocation and energy transfer. The key insight of our algorithm is to translate the original, intractable joint-optimization problem into a problem space where a good approximate solution can be quickly found. We evaluate our scheme through theoretical analysis and simulation. The evaluation shows that our approach improves the system utility by over 20% compared to start-of-the-art in dense IoT HetNets. Compared to alternative schemes, our approach maintains the best user fairness and rate experience and can solve the problem in a fast and scalable way.Index Terms-Downlink/Uplink decoupling (DUDe), Enhanced inter-cell interference coordination (eICIC), Simultaneous wireless information and power transfer (SWIPT), Internet of Things (IoT), heterogeneous network (HetNet).

A Survey on Multiuser SWIPT Communications for 5G+

“…Non-linear EH model is also considered in [130] for HetNets in the presence of multi-carrier transmission. In [131], the authors have considered an IoT HetNet in a densely deployed transmission scenario where uplink and downlink transmissions are decoupled to increase the target utility. Finally, the authors in [132] consider a SWIPT enabled multi-tier network with a cooperative NOMA transmission scenario where BS distribution follows a Poisson point process model.…”

Very large scale integration architectures of discrete transforms for digital signal processing applications

“…5G has been designed to meet ambitious performance benchmarks, including 10 Gb/s uplink peak data rate, 20 Gb/s downlink peak data rate, 5 ms end-to-end latency, and 99.999% end-to-end reliability, among others [1]- [3]. Several promising key technologies, such as massive multiple-input-multiple-output (mMIMO), millimeter wave (mmWave) [4], and ultra-dense networking (UDN), have been implemented to meet these 5G requirements [5]. The anticipated sixth generation (6G) of wireless networks, expected to serve communication needs beyond 2030, have sparked significant research interest due to the advent of novel requirements following the rapid evolution of wireless applications [6]- [12].…”

“…The so-called deep learning algorithms may provide low-complexity solutions with a small delay, where the parallel CNN units have been conducted for antenna selection [111]. Furthermore, resource allocation problems were by federated learning [5], [188] and reinforcement learning [192].…”