The advent of 5G positioning techniques by 3GPP has unlocked possibilities for applications in public safety, vehicular systems, and location-based services. However, these applications demand accurate and reliable positioning performance, which has led to the proposal of newer positioning techniques. To further advance the research on these techniques, in this paper, we develop a 3GPP-compliant 5G positioning testbed, incorporating gNodeBs (gNBs) and User Equipment (UE). The testbed uses New Radio (NR) Positioning Reference Signals (PRS) transmitted by the gNB to generate Time of Arrival (TOA) estimates at the UE. We mathematically model the inter-gNB and UE-gNB time offsets affecting the TOA estimates and examine their impact on positioning performance. Additionally, we propose a calibration method for estimating these time offsets. Furthermore, we investigate the environmental impact on the TOA estimates. Our findings are based on our mathematical model and supported by experimental results.

For energy-neutral operation (ENO) of wireless sensor networks (WSNs), we consider a WSN deployed in a wireless powered communication network. In this network, sensor nodes with high harvesting energies and good link budgets have energy remaining after sending their data to the cluster head (CH), whereas the CH suffers from energy scarcity. Thus, we apply the simultaneous wireless information and power transfer (SWIPT) technique so that the sensor nodes can transfer their remaining energy to the CH while transmitting data in a cooperative manner. To maximize the achievable rate of sensing data while guaranteeing ENO, we propose a novel ENO framework, which provides a frame structure for SWIPT operation, rate improvement subject to ENO, SWIPT ratio optimization, as well as clustering and CH selection algorithm. The results of extensive simulations demonstrate that the proposed scheme based on SWIPT significantly improves the achievable rate while guaranteeing ENO, in comparison with the conventional schemes without SWIPT.

Low-power wide-area (LPWA) networks have been proposed to satisfy the features of massive machine-type communication (mMTC) of Internet-of-Things (IoT) networks with the large number of end devices, such as low data rates, high network scalability, wide area coverage, and delay tolerance with very low energy cost. The LoRa wide-area network (LoRaWAN) is one of the leading technologies among LPWA networks (LPWANs) and supports three types of medium access control (MAC) options: Class A, Class B, and Class C, each of which are used to address different needs of various applications. Specifically, Class B is designed to reduce downlink frame transmission delay while the end device maintains a relatively low energy consumption. In this article, we propose an analytical model of LoRaWAN Class B mode, focusing on the delay, the data throughput, and the energy consumption for downlink frame transmission using the M/G/1 queueing model. Based on the analytical model, a cost function considering both the average waiting time of the frame in the gateway and the average energy consumption of an end device is proposed. Using the cost function, we derive the optimal number of ping slots which maximizes the value of the cost function. Results show the tradeoff relation between the waiting time of the frame in the gateway and the energy consumption of an end device, and it is verified that the optimal number of ping slots increases as the traffic density increases.

The spontaneous emergence of natural phenomena such as flies flashing, the cardiac pacemaker cells, cooperative searching for food by bees, the behavior of flocking birds, schooling of fish, ants, and termites, and frogs' calling behavior shows various synchronization. Some synchronization principals have been investigated and analyzed from this natural phenomena in order to be applied to the various wireless communication system. In this study, we herein propose the resource allocation scheme based on the frogs' calling behavior algorithm, which enable to achieve strict desynchronization among nodes in a fully connected network in a distributed manner. By mapping the phase difference of two consecutive nodes to the amount of resource allocated to a node, we can obtain distributed and fair resource allocation among nodes in a network. Simulation results show that the proposed algorithm achieves strict desynchronization (thus fair resource allocation) over all nodes in a network in a distributed manner.