The fifth generation (5G) and beyond networks have the potential to meet the increasing demands of emerging applications and industry verticals for high throughputs and reliable low latency communication under various mobility scenarios. Also, there is a great demand for new user experiences that would require greater uplink performance from these networks. To meet these expectations from 5G networks, critical gaps need to be filled before they can be fully implemented for mobile applications in complex environments like smart cities. Millimeter wave frequencies can significantly increase the performance of these networks, but they also suffer from reliability issues due to limited transmission range. Many uplink enhancement technologies, like multi-connectivity, carrier aggregation, etc., have been proposed to improve the performance, but they all either add to the user equipment (UE) complexity or increase the deployment costs. We develop a novel multilink scheme for the uplink where the UE performs only a single transmission on a common channel, and every base station that receives this signal would accept and process it. Our solution reduces frequent handovers and allows high UE mobility with no additional complexity. We propose and evaluate this novel 5G deployment with multilink using the Simu5G network simulator for enabling future 5G systems. The proposed 5G architecture is shown to meet the requirements of next-generation applications. Our simulations show that the proposed technique improves the throughput by up to 54%, the latency by up to 27.61%, and the reliability by up to 91.36% compared to a single link system.

The fifth generation (5G) networks and beyond are key to meeting the exponentially increasing demands of next generation services for high throughput and reliable low latency communication under various mobility scenarios. These promising features have critical gaps to be filled before they can be fully implemented for mobile applications in complex environments like smart cities. Millimeter wave (mmWave) communications is a key enabler for a significant increase in the performance of these networks. However, due to the extremely limited transmission range of mmWave frequencies, 5G network deployments are designed to have several small cells operating in the mmWave frequency range using Ultra-dense networking (UDN) techniques to provide continuous coverage. But, such deployments not only face challenges in terms of a higher number of handovers, higher latency, lower reliability, and higher interference levels but also in terms of site acquisition, logistics, unbalanced load distributions, and power requirements. Multi-connectivity can improve the performance of UDNs and provide better deployment strategies as it provides multiple simultaneous links between the User Equipment (UE) and base stations. In such systems, packet duplication can be used to meet the stringent reliability and latency requirements of modern cellular networks as data packets are duplicated and transmitted concurrently over two or more independent links. The downside to packet duplication, however, is the increased usage of resources like spectrum, power, etc. In this work, we explore and analyze different techniques that could aid in reducing radio resource utilization without sacrificing the improvements in reliability and latency observed through packet duplication. To perform this study, we develop a novel 5G deployment with new radio dual connectivity (NR-DC) and packet duplication to improve reliability. We then analyze possible enhancements in the system to improve radio resource utilization when packet duplication is implemented. In this article, we propose and evaluate this novel 5G network deployment with multi-connectivity using Simu5G network simulator for enabling future 5G systems. The proposed 5G architecture is shown to meet the requirements of next generation applications. Our simulations show that the proposed techniques improve the throughput by up to 165.72%, the latency by up to 91.22%, and the packet loss decreases to near zero compared to a single link system.

The fifth generation (5G) networks and beyond enable technologies that are key to meeting the exponentially increasing demands of next generation services for reliable low latency communication under various mobility scenarios. Millimeter wave technology is a key enabler for 5G use cases; however, mmWave links have a lower transmission range and are subject to blockages. Multi-connectivity (MC) enhances the system performance by providing multiple simultaneous links between the user equipment (UE) and the base stations. In such systems, packet duplication can be used to meet the stringent reliability and latency requirements of modern cellular networks as data packets are duplicated and transmitted concurrently over two independent links. We propose and evaluate a novel 5G deployment with new radio dual connectivity (NR-DC) and packet duplication using the Simu5G network simulator for enabling future 5G systems, especially in mobility scenarios. We also analyze possible enhancements in the system to improve the radio resource utilization when packet duplication is implemented. Our simulation results show the improvement in reliability and latency that NR-DC with optimized packet duplication could bring to next generation applications and services like enhanced vehicle-to-everything (V2X) scenarios.

Operational medical environments require reliable hands-free solutions to extract data from audio captured under noisy scenarios during rescue missions and provide timely information. However, approaches using automatic speech recognition (ASR) and natural language processing (NLP) techniques are complex as these conversations have a wide range of noise, involve medical terms from multiple speakers, and occur in high-stress environments, among others. These are further complicated by the lack of large training datasets for operational medical scenarios. To address these issues, we developed a platform that enables resilient hands-free data collection, preserves complete documentation through stages of care, and presents the information in near real-time, critical for the medical operation. Our work uniquely focused on systematic evaluation and improvement of a deep neural network-based ASR system by leveraging realistic testing data obtained from medical simulations of battlefield scenarios, which to our knowledge have not been addressed in any prior work. The system performance is shown to improve significantly using multi-style training, language model adaptation for the medical domain, speech enhancement, and NLP techniques.