An official website of the United States government
WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. The high-frequency bands can provide high transmission rates, but their high amplitude makes it so the signal cannot go through any walls or obstacles. The signal also has a strong path loss element caused by the high frequency, significantly limiting the reach of connections because the signal is too weak at moderate distances. Due to these issues, users can easily lose connection with the access point while moving and need to connect to a new device, making WiGig systems unstable as they need to rely on frequent handovers to maintain a high-quality service. However, this solution is problematic as it forces users into bad connections and downtime before they are switched to a better access point. In this work, we use machine learning to identify patterns in user behaviors and predict user actions. This prediction is used to do proactive handovers, switching users to access points with better future transmission rates and a more stable environment based on the future state of the user. Results show that not only the proposal is effective at predicting channel data, but the use of such predictions improves system performance and avoids unnecessary handovers.
more »
« less
This content will become publicly available on November 24, 2026
OPCM: Opportunistic Performance-driven Connectivity Management for 5G/xG Networks
5G and future 6G networks deploy cells with diverse combinations of access technologies, architectures, and radio frequency bands/channels. Cellular operators also employ carrier aggregation for higher data access speeds. We investigate the fundamental question of how to intelligently and dynamically configure and reconfigure a user equipment's serving cells to deliver the best network performance. Through comprehensive measurements across 12 cities in 5 countries, we experimentally show the wide availability, heterogeneity, and untapped performance gains of today's cell deployments. We then present a principled, performance-driven connectivity management framework, dubbed OPCM. It is a centralized solution deployed at the base station, allowing it to coordinate multiple UEs, enforce operator policies, and facilitate user fairness. Extensive evaluations show that OPCM improves the application QoE by up to 65.2%.
more »
« less
- PAR ID:
- 10659459
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Networking
- Volume:
- 3
- Issue:
- CoNEXT4
- ISSN:
- 2834-5509
- Page Range / eLocation ID:
- 1 to 23
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Network quality-of-service (QoS) does not always translate to user quality-of-experience (QoE). Consequently, knowledge of user QoE is desirable in several scenarios that have traditionally operated on QoS information. Examples include traffic management by ISPs and resource allocation by the operating system. But today these systems lack ways to measure user QoE. To help address this problem, we propose offline generation of per-app models mapping app-independent QoS metrics to app-specific QoE metrics. This enables any entity that can observe an app's network traffic-including ISPs and access points-to infer the app's QoE. We describe how to generate such models for many diverse apps with significantly different QoE metrics. We generate models for common user interactions of 60 popular apps. We then demonstrate the utility of these models by implementing a QoE-aware traffic management framework and evaluate it on a WiFi access point. Our approach successfully improves QoE metrics that reflect user-perceived performance. First, we demonstrate that prioritizing traffic for latency-sensitive apps can improve responsiveness and video frame rate, by 46% and 115%, respectively. Second, we show that a novel QoE-aware bandwidth allocation scheme for bandwidth-intensive apps can improve average video bitrate for multiple users by up to 23%.more » « less
-
null (Ed.)We introduce non-hierarchical caching (NHC), a novel approach to caching in modern storage hierarchies. NHC improves performance as compared to classic caching by redirecting excess load to devices lower in the hierarchy when it is advantageous to do so. NHC dynamically adjusts allocation and access decisions, thus maximizing performance (e.g., high throughput, low 99%-ile latency). We implement NHC in Orthus-CAS (a block-layer caching kernel module) and Orthus-KV (a user-level caching layer for a key-value store). We show the efficacy of NHC via a thorough empirical study: Orthus-KV and Orthus-CAS offer significantly better performance (by up to 2x) than classic caching on various modern hierarchies, under a range of realistic workloads.more » « less
-
We propose a mechanism for unlicensed LTE channel selection that not only takes into account interference to and from Wi-Fi access points but also considers other LTE operators in the unlicensed band. By collecting channel utilization statistics and sharing this information periodically with other unlicensed LTE eNBs, each eNB can improve their channel selection given their limited knowledge of the full topology. While comparing our algorithm to existing solutions, we find that the similarity between sensed Wi-Fi occupation at neighboring eNBs greatly impacts the performance of channel selection algorithms. To achieve better performance across diverse scenarios, we expand on our statistical channel selection formulation to include reinforcement learning, thereby balancing the shared contextual information with historical performance. We simulate operation in the unlicensed band using our channel selection algorithm and show how Wi-Fi load and inter-cell interference estimation can jointly be used to select transmission channels for all small cells in the network. Our approaches lead to an increase in user-perceived throughput and spectral efficiency across the entire band when compared to the greedy channel selection.more » « less
-
We address the uplink small-cell access point (AP) placement problem for optimal throughput, while considering load balancing (LB) among the APs. To consider LB and consequently incorporate fairness in user spectral access, i.e., the frequency of user-to-AP communications, we modify the Lloyd algorithm from vector quantization so that delays incurred by the existence of a large number of users in a cell are accounted for in the AP placement process. Accordingly, we present two methods, the first of which involves the incorporation of weights proportional to the cell occupancy, hence called the Occupancy Weighted Lloyd algorithm (OWLA). The second method adds a new step to the Lloyd algorithm, which involves re-assigning users from higher to lower occupancy cells, and the adoption of a distance threshold to cap the throughput lost in the assignment process. This formulated Lloyd-type algorithm is called the Cell Equalized Lloyd Algorithm-α (CELA-α) where α is a factor that allows for throughput and spectrum access delay trade-off. Extensive simulations show that both CELA-α and OWLA algorithms provide significant gains, in comparison to the standard Lloyd algorithm, in 95%-likely user spectral access. For the α values considered in this paper, CELA-α achieves gains up to 20.83%, while OWLA yields a gain of 12.5%. Both algorithms incur minimal throughput losses of different degrees, and the choice of using one algorithm over the other for AP placement depends on system LB as well as throughput requirements.more » « less
