Enabling High Throughput and Reliable Low Latency Communication over Vehicular Mobility in Next-Generation Cellular Networks

The fifth-generation (5G) networks and beyond need paradigm shifts to realize the exponentially increasing demands of next-generation services for high throughputs, low latencies, and reliable communication under various mobility scenarios. However, these promising features have critical gaps that need to be filled before they can be fully implemented for mobile applications in complex environments like smart cities. Although the sub-6 GHz bands can provide reliable and larger coverage, they cannot provide high data rates with low latencies due to a scarcity of spectrum available in these bands. Millimeter wave (mmWave) communication is a key enabler for a significant increase in the performance of these networks due to the availability of large bands of spectrum. However, the extremely limited transmission range of mmWave frequencies leads to poor reliability, especially for mobility scenarios. In this work, we present and evaluate the solutions in three key areas for achieving high throughput along with reliable low latency connection, especially for mobility scenarios in next-generation cellular networks. To enable the 5G networks to meet the demanding requirements of cellular networks, we look into (1) multi-connectivity for enhancing the performance of next-generation cellular networks, (2) designing a reliable network using multi-connectivity, and (3) developing a multilink scheme with efficient radio resource management. Despite the technological advances made in the design and evolution of 5G networks, emerging services impose stringent requirements which have not been fully met by 5G networks so far. The work in this dissertation aims to explore the challenges of future networks and address the needs in the three areas listed above. The results of the study open opportunities to resolve real-world 5G network issues. As 5G networks need to fulfill the rising performance demands of upcoming applications and industry verticals, we first study and evaluate multi-connectivity, which involves simultaneous connectivity with multiple radio access technologies or multiple bands, as a key enabler in improving the performance of the 5G networks. 5G networks are designed to have several small cells operating in the mmWave frequency range using ultra-dense networks (UDN) deployments to provide continuous coverage. But, such deployments not only face challenges in terms of frequent handovers, higher latency, lower reliability, and higher interference levels but also in terms of increasing complexity and cost of deployment, unbalanced load distributions, and power requirements. To address the challenges in high density base station deployments, we study and evaluate novel deployment strategies using multi-connectivity. In NR-NR Dual Connectivity (NR-DC), the user equipment (UE) is connected simultaneously to two gNBs, with one acting as the master node and the other as the secondary node to improve the performance of the 5G system. The master node operating at the sub-6 GHz bands provides high reliability, and the secondary node using the high bandwidth mmWave bands provides the high throughputs expected of 5G networks. This deployment also improves the latency as it decreases the number of handovers and link establishments. Thus, in this dissertation, we propose and evaluate novel 5G deployments with multi-connectivity, which can be used to ensure that these 5G networks are able to meet the demanding requirements of future services. The 5G networks also need to support ultra-reliable low latency communication, which refers to using the network for mission-critical communication that requires high reliability along with low latency. However, technological advancements so far have not been able to fully meet all these requirements. Thus, in this work, we design a reliable 5G network using multi-connectivity, which can simultaneously support high throughputs along with ultra-reliable low latency communication. Deployments using mmWave bands are highly susceptible to channel fluctuations and blockages. Thus, it is critical to consider new techniques and approaches that address these needs and can be implemented practically. In this work, we propose and implement a novel approach using packet duplication and its optimization in an NR-DC system to improve the performance of the system. In an NR-DC deployment with packet duplication, multiple instances of a packet are generated and transmitted simultaneously over different uncorrelated channels between the UE and the base stations, which decreases the packet failure probability. We also propose enhancements to the packet duplication feature for efficient radio resource utilization by looking into the distance of the UE from the base station, the velocity of the UE, and the received signal strength indicator (RSSI) levels. The proposed system improves the performance in terms of throughput, latency, and reliability under varying mobility scenarios. Finally, the 5G networks need to meet the increasing demands of uplink data traffic for applications such as autonomous driving, IoT applications, live video, etc. However, the uplink performance is lower compared to the downlink, and hence, it is critical for 5G to improve uplink performance. Thus, there are open research questions on what should be the network architecture with efficient radio resource utilization to meet the stringent requirements for mobility scenarios. In this work, we propose a novel uplink scheme where the UE performs only a single transmission on a common channel, and every base station that can receive this signal would accept and process it. This technique increases the probability of successful transmission and hence, increases the reliability of the network. It also removes the need to perform frequent handovers and allows high mobility with reduced latency. In this work, we propose and evaluate novel approaches for improving the performance of next-generation networks, which will be a key enabler for future applications. The proposed 5G techniques are shown to significantly improve the throughput, latency, and reliability simultaneously and are able to fulfill the stringent requirements of future services. Our work focuses on developing novel solutions for addressing the challenges involved in building next-generation cellular networks. In the future, we plan to further develop our system for real-world city-scale deployments

The Gaussian Approximation to Multiple-Access Interference in the Evaluation of the Performance of a Communication System with Convolutional Coding and Viterbi Decoding

The standard Gaussian approximation is extended to the performance analysis of direct-sequence code-division multiple-access (DS-CDMA) systems using binary con-volutional coding, quaternary modulation with quaternary direct-sequence spreading and Viterbi decoding. Using the standard Gaussian approximation, the random variables mod-eling the multiple-access interference in the receiver statistics are replaced by an equivalent additive Gaussian noise term with the same variance as the actual multiple-access interfer-ence term. The Gaussian approximation is shown to result in an accurate approximation to the probability of code-word error at the receiver. The accuracy is demonstrated by compar-ing simulation results for the actual multiple-access system and a model using the standard Gaussian approximation. Both are compared with two previously developed closed-form bounds on the performance: the concave-first-event bound and the concave-integral bound

Container Resource Allocation versus Performance of Data-intensive Applications on Different Cloud Servers

In recent years, data-intensive applications have been increasingly deployed on cloud systems. Such applications utilize significant compute, memory, and I/O resources to process large volumes of data. Optimizing the performance and cost-efficiency for such applications is a non-trivial problem. The problem becomes even more challenging with the increasing use of containers, which are popular due to their lower operational overheads and faster boot speed at the cost of weaker resource assurances for the hosted applications. In this paper, two containerized data-intensive applications with very different performance objectives and resource needs were studied on cloud servers with Docker containers running on Intel Xeon E5 and AMD EPYC Rome multi-core processors with a range of CPU, memory, and I/O configurations. Primary findings from our experiments include: 1) Allocating multiple cores to a compute-intensive application can improve performance, but only if the cores do not contend for the same caches, and the optimal core counts depend on the specific workload; 2) allocating more memory to a memory-intensive application than its deterministic data workload does not further improve performance; however, 3) having multiple such memory-intensive containers on the same server can lead to cache and memory bus contention leading to significant and volatile performance degradation. The comparative observations on Intel and AMD servers provided insights into trade-offs between larger numbers of distributed chiplets interconnected with higher speed buses (AMD) and larger numbers of centrally integrated cores and caches with lesser speed buses (Intel). For the two types of applications studied, the more distributed caches and faster data buses have benefited the deployment of larger numbers of containers

Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study

BACKGROUND: Prehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients. OBJECTIVE: The aim of this study is to develop a mobile platform for hands-free prehospitalization documentation to assist first responders in operational medical environments by aggregating all existing solutions for noise resiliency and domain adaptation. METHODS: The platform was built to extract meaningful medical information from the real-time audio streaming at the point of injury and transmit complete documentation to a field hospital prior to patient arrival. To this end, the state-of-the-art automatic speech recognition (ASR) solutions with the following modular improvements were thoroughly explored: noise-resilient ASR, multi-style training, customized lexicon, and speech enhancement. The development of the platform was strictly guided by qualitative research and simulation-based evaluation to address the relevant challenges through progressive improvements at every process step of the end-to-end solution. The primary performance metrics included medical word error rate (WER) in machine-transcribed text output and an F1 score calculated by comparing the autogenerated documentation to manual documentation by physicians. RESULTS: The total number of 15,139 individual words necessary for completing the documentation were identified from all conversations that occurred during the physician-supervised simulation drills. The baseline model presented a suboptimal performance with a WER of 69.85% and an F1 score of 0.611. The noise-resilient ASR, multi-style training, and customized lexicon improved the overall performance; the finalized platform achieved a medical WER of 33.3% and an F1 score of 0.81 when compared to manual documentation. The speech enhancement degraded performance with medical WER increased from 33.3% to 46.33% and the corresponding F1 score decreased from 0.81 to 0.78. All changes in performance were statistically significant (P\u3c.001). CONCLUSIONS: This study presented a fully functional mobile platform for hands-free prehospitalization documentation in operational medical environments and lessons learned from its implementation