Mid Sweden University

With the increasing number of Internet-of-things (IoT) devices, the need for energy-efficient and spectrum-efficient networks that can support resource-constrained devices within existing wireless infrastructures becomes critical. This paper investigates the application of deep reinforcement learning (DRL) algorithms to optimize the energy efficiency (EE) of a secondary device (SD) equipped with radio frequency energy harvesting (RF-EH) antennas. The system models a wireless powered communication network (WPCN) where the SD employs a cognitive-radio non-orthogonal multiple access (CR-NOMA) scheme to transmit data during uplink communications of neighboring primary devices (PDs). Among the DRL approaches evaluated, proximal policy optimization (PPO) emerged as the most effective, achieving the highest EE values and demonstrating its suitability for this problem. Additionally, our results show that equal gain combining (EGC) consistently achieves superior EE compared to other diversity-combining techniques, making it a favorable choice for self-sustaining IoT networks. These findings provide valuable insights into the role of diversity-combining techniques and DRL algorithms in enhancing SD performance in dynamic EH environments.

This paper presents a novel approach to maximizing the throughput of self-sustaining mobile IoT devices using a quality-of-service (QoS)-aware non-orthogonal multiple access (NOMA) technique. The proposed method enables transmissions within the timeslots of licensed users in IoT networks through a deep reinforcement learning (DRL)-driven strategy. By integrating hybrid energy harvesting (EH) from radio frequency (RF) and solar sources, the proposed framework is designed to optimize the energy usage and data transmission rates of a mobile sensing node (MSN) operating in a dynamic wireless environment. Our model incorporates non-linear RF and solar EH characteristics and accounts for mobility-induced variations in channel conditions. The throughput maximization problem is decomposed into a two-layer optimization framework, where the first layer utilizes convex optimization for power and time-sharing coefficients, while the second layer employs DRL to adapt to one-dimensional state-action spaces. Our results show that the Prioritized Experience Replay (PER)-DDPG algorithm achieves the best performance among the evaluated DRL approaches by enabling hybrid EH to achieve 7.83% higher data rates compared to RF-only scenarios. The results underscore the effectiveness of the DRL-based approach in enabling continuous operation and enhanced data rates for mobile IoT applications in QoS-aware NOMA IoT networks. 

This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multipoint non-orthogonal multiple access (CoMP-NOMA) networks. We consider a system model where a UAV-mounted RIS assists in serving multiple users through NOMA while coordinating with multiple base stations. The optimization of UAV trajectory, RIS phase shifts, and NOMA power control constitutes a complex problem due to the hybrid nature of the parameters, involving both continuous and discrete values. To tackle this challenge, we propose a novel framework utilizing the multi-output proximal policy optimization (MO-PPO) algorithm. MO-PPO effectively handles the diverse nature of these optimization parameters, and through extensive simulations, we demonstrate its effectiveness in achieving near-optimal performance and adapting to dynamic environments. Our findings highlight the benefits of integrating ARIS in CoMP-NOMA networks for improved spectral efficiency and coverage in future wireless networks.

Intelligent reflecting surface (IRS) is considered as a key technology for the sixth generation (6G) networks for creating a controlled environment for users to achieve higher data rates, throughput, and energy efficiency. IRS-assisted networks provide indirect line-of-sight (LoS) to non line-of-sight (NLoS) users, enabling them to achieve higher data rates. The purpose of this paper is to investigate the performance of IRS-assisted ultra-high frequency (UHF) networks in actual rural, sub-urban, and urban environment, in terms of rate coverage probability, spectral and energy efficiency. The actual building locations of all three locations with their heights are modeled as blockages. The analysis is carried out for different densities of BSs and IRS surfaces for different number of mobile users. Our analysis underlines the difference between the coverage probability of 2D versus 3D building areas. It also shows that the deployment of IRS surfaces significantly increases the rate per unit area of the conventional BS networks in practical environments. Our simulation results provide the optimal number of IRS elements and mobile users to achieve a certain rate coverage probability with maximum energy efficiency. Our results also highlight the optimal number of BSs and IRS surfaces for each location that can be deployed to achieve higher energy efficiency. 

Intelligent reflecting surfaces (IRSs) have been considered to revolutionize beyond 5G and 6G systems as they help increase signal strength through their ability to control radio environments effectively. Introducing IRS assistance in a single-input single-output (SISO) network has been proven to improve the system's performance. This paper compares the performance of a single IRS-assisted SISO system against a double IRS-assisted system under various wireless network setups. Our work relies on a shared allocation scheme of IRS elements, where a practical phase-dependent amplitude phase shift model is utilized along with discrete phase shifts to develop a reliable and energy-efficient system. We observe energy efficiency while altering system parameters to identify the limits where each system works better. The simulation results show that two IRSs perform better at large deployments, whereas a single IRS performs better in compact environments. 

Zero-energy radios in energy-constrained devices are envisioned as key enablers to realizing the next-generation Internet-of-things (NG-IoT) networks for ultra-dense sensing and monitoring. This paper presents analytical modeling and analysis of the energy-efficient uplink transmission of an energyconstrained secondary sensor operating opportunistically among several primary sensors. The considered scenario assumes that all primary sensors transmit in a round-robin, time division multiple access-based schemes, and the secondary sensor is admitted in the time slot of each primary sensor using a nonorthogonal multiple access technique, inspired by cognitive radio. The energy efficiency of the secondary sensor is maximized by exposing it to a deep reinforcement learning-based algorithm, recognized as a deep deterministic policy gradient (DDPG). Our results demonstrate that the DDPG-based transmission scheme outperforms the conventional random and greedy algorithms in terms of energy efficiency at different operating conditions. 

In this paper, we consider a unmanned aerial vehicle (UAV)-enabled multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system for a smart city environment, where a multi-Antenna UAV acts as an amplify-And-forward (AF) relay to extend the coverage to several disconnected users aided by NOMA. We present an optimization problem that jointly determines the location of the UAV, optimal beamforming at the UAV and power allocation at the base station (BS), in order to maximize the system sum-rate. Due to the severe non-convexity of the problem, we decouple the problem into three sub-problems and solve them sequentially. First, the optimal location of the UAV is determined by minimizing the total path loss. Next, the UAV beamforming is solved by transforming the problem into second-order cone programming (SOCP) and finally the power allocation at the BS is determined using linear-fractional programming (LFP). The simulation results demonstrate that the proposed scheme performs better than the baseline schemes and orthogonal multiple access (OMA) scheme.