An official website of the United States government
Fifth generation mobile communication systems (5G) have to accommodate both Ultra-Reliable Low-Latency Communication (URLLC) and enhanced Mobile Broadband (eMBB) services. While eMBB applications support high data rates, URLLC services aim at guaranteeing low-latencies and high-reliabilities. eMBB and URLLC services are scheduled on the same frequency band, where the different latency requirements of the communications render their coexistence challenging. In this survey, we review, from an information theoretic perspective, coding schemes that simultaneously accommodate URLLC and eMBB transmissions and show that they outperform traditional scheduling approaches. Various communication scenarios are considered, including point-to-point channels, broadcast channels, interference networks, cellular models, and cloud radio access networks (C-RANs). The main focus is on the set of rate pairs that can simultaneously be achieved for URLLC and eMBB messages, which captures well the tension between the two types of communications. We also discuss finite-blocklength results where the measure of interest is the set of error probability pairs that can simultaneously be achieved in the two communication regimes.
more »
« less
This content will become publicly available on July 2, 2025
Intelligent Dynamic Resource Allocation and Puncturing for Next Generation Wireless Networks
As we progress from 5G to emerging 6G wireless, the spectrum of cellular communication services is set to broaden significantly, encompassing real-time remote healthcare applications and sophisticated smart infrastructure solutions, among others. This expansion brings to the forefront a diverse set of service requirements, underscoring the challenges and complexities inherent in next-generation networks. In the realm of 5G, Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) have been pivotal service categories. As we venture into the 6G era, these foundational use cases will evolve and embody additional performance criteria, further diversifying the network service portfolio. This evolution amplifies the necessity for dynamic and efficient resource allocation strategies capable of balancing the diverse service demands. In response to this need, we introduce the Intelligent Dynamic Resource Allocation and Puncturing (IDRAP) framework. Leveraging Deep Reinforcement Learning (DRL), IDRAP is designed to balance between the bandwidth-intensive requirements of eMBB services and the latency and reliability needs of URLLC users. The performance of IDRAP is evaluated and compared against other resource management solutions, including Intelligent Dynamic Resource Slicing (IDRS), Policy Gradient Actor-Critic Learning (PGACL), System-Wide Tradeoff Scheduling (SWTS), Sum-Log, and Sum-Rate.The results show an improved Service Satisfaction Level (SSL) for eMBB users while maintaining the essential SSL threshold for URLLC services.
more »
« less
- Award ID(s):
- 2030291
- PAR ID:
- 10541381
- Publisher / Repository:
- IEEE/ieeeXplore
- Date Published:
- Journal Name:
- IEEE Internet of Things Journal
- Volume:
- 11
- Issue:
- 19
- ISSN:
- 2327-4662
- Page Range / eLocation ID:
- 31438 - 31452
- Subject(s) / Keyword(s):
- Deep learning, enhanced mobile broadband (eMBB), fairness, puncturing, Q-learning, reinforcement learning (RL), scheduling, throughput, ultra-reliable low-latency communications (URLLCs)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Interactive mobile applications like web browsing and gaming are known to benefit significantly from low latency networking, as applications communicate with cloud servers and other users' devices. Emerging mobile channel standards have not met these needs: 5G's general-purpose eMBB channel has much higher bandwidth than 4G but empirically offers little improvement for common latency-sensitive applications, while its ultra-low-latency URLLC channel is targeted at only specific applications with very low bandwidth requirements. We explore a different direction for wireless channel design to address the fundamental bandwidth-latency tradeoff: utilizing two channels -- one high bandwidth, one low latency -- simultaneously to improve performance of common Internet applications. We design DChannel, a fine-grained packet-steering scheme that takes advantage of these parallel channels to transparently improve application performance. With 5G channels, our trace-driven and live network experiments show that even though URLLC offers just 1% of the bandwidth of eMBB, using both channels can improve web page load time and responsiveness of common mobile apps by 16-40% compared to using exclusively eMBB. This approach may provide service providers important incentives to make low latency channels available for widespread use.more » « less
-
Interactive mobile applications like web browsing and gaming are known to benefit significantly from low latency networking, as applications communicate with cloud servers and other users' devices. Emerging mobile channel standards have not met these needs: 5G's general-purpose eMBB channel has much higher bandwidth than 4G but empirically offers little improvement for common latency-sensitive applications, while its ultra-low-latency URLLC channel is targeted at only specific applications with very low bandwidth requirements. We explore a different direction for wireless channel design to address the fundamental bandwidth-latency tradeoff: utilizing two channels -- one high bandwidth, one low latency -- simultaneously to improve performance of common Internet applications. We design DChannel, a fine-grained packet-steering scheme that takes advantage of these parallel channels to transparently improve application performance. With 5G channels, our trace-driven and live network experiments show that even though URLLC offers just 1% of the bandwidth of eMBB, using both channels can improve web page load time and responsiveness of common mobile apps by 16-40% compared to using exclusively eMBB. This approach may provide service providers important incentives to make low latency channels available for widespread use.more » « less
-
Emerging 5G systems will need to efficiently support both enhanced mobile broadband traffic (eMBB) and ultra-low- latency communications (URLLC) traffic. In these systems, time is divided into slots which are further sub-divided into minislots. From a scheduling perspective, eMBB resource allocations occur at slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively overlapped at the minislot timescale, resulting in selective superposition/puncturing of eMBB allocations. This approach enables minimal URLLC latency at a potential rate loss to eMBB traffic. We study joint eMBB and URLLC schedulers for such systems, with the dual objectives of maximizing utility for eMBB traffic while immediately satisfying URLLC demands. For a linear rate loss model (loss to eMBB is linear in the amount of URLLC superposition/puncturing), we derive an optimal joint scheduler. Somewhat counter-intuitively, our results show that our dual objectives can be met by an iterative gradient scheduler for eMBB traffic that anticipates the expected loss from URLLC traffic, along with an URLLC demand scheduler that is oblivious to eMBB channel states, utility functions and allocation decisions of the eMBB scheduler. Next we consider a more general class of (convex/threshold) loss models and study optimal online joint eMBB/URLLC schedulers within the broad class of channel state dependent but minislot-homogeneous policies. A key observation is that unlike the linear rate loss model, for the convex and threshold rate loss models, optimal eMBB and URLLC schedul- ing decisions do not de-couple and joint optimization is necessary to satisfy the dual objectives. We validate the characteristics and benefits of our schedulers via simulation.more » « less
-
Fifth Generation (5G) networks operating on mmWave frequency bands are anticipated to provide an ultrahigh capacity with low latency to serve mobile users requiring high-end cellular services and emerging metaverse applications. Managing and coordinating the high data rate and throughput among the mmWave 5G Base Stations (BSs) is a challenging task, and it requires intelligent network traffic analysis. While BSs coordination has been traditionally treated as a centralized task, this involves higher latency that may adversely impact the user’s Quality of Service (QoS). In this paper, we address this issue by considering the need for distributed coordination among BSs to maximize spectral efficiency and improve the data rate provided to their users via embedded AI. We present Peer-Coordinated Sequential Split Learning dubbed PC-SSL, which is a distributed learning approach whereby multiple 5G BSs collaborate to train and update deep learning models without disclosing their associated mobile users data, i.e., without privacy leakage. Our proposed PC-SSL minimizes the data transmitted between the client BSs and a server by processing data locally on the clients. This results in low latency and computation overhead in making handoff decisions and other networking operations. We evaluate the performance of our proposed PC-SSL in the mmWave 5G throughput prediction use-case based on a real dataset. The results demonstrate that our proposal outperforms conventional approaches and achieves a comparable performance to centralized, vanilla split learning.more » « less
