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Offloading cellular traffic to WiFi networks plays an important role in alleviating the increasing burden on cellular networks. However, excessive traffic offloading brings severe packet collisions into a WiFi network due to its contention-based medium access scheme, which significantly reduces the WiFi network’s throughput. In this paper, we propose DAO, a device-to-device (D2D) communications assisted traffic offloading scheme to improve the amount of traffic offloaded from cellular to WiFi in integrated cellular and WiFi networks. Specifically, in an integrated cellular-WiFi network, the cellular network exploits D2D communications in licensed cellular bands to aggregate traffic from cellular users before offloading it to the WiFi network to reduce the number of contending users in WiFi access. The traffic offloading process in DAO is formulated as an optimization problem that jointly takes into account the activations of aggregation nodes (ANs) and the connections between ANs and offloading users to maximize the offloaded traffic while guaranteeing the long-term data rates required by the offloading users. Extensive simulation results reveal the significant performance gain achieved by DAO over the existing schemes.
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QCell: Self-optimization of Softwarized 5G Networks through Deep Q-learning
With the unprecedented rise in traffic demand and mobile subscribers, real-time fine-grained optimization frameworks are crucial for the future of cellular networks. Indeed, rigid and inflexible infrastructures are incapable of adapting to the massive amounts of data forecast for 5G networks. Network softwarization, i.e., the approach of controlling “everything” via software, endows the network with unprecedented flexibility, allowing it to run optimization and machine learning-based frame- works for flexible adaptation to current network conditions and traffic demand. This work presents QCell, a Deep Q-Network- based optimization framework for softwarized cellular networks. QCell dynamically allocates slicing and scheduling resources to the network base stations adapting to varying interference con- ditions and traffic patterns. QCell is prototyped on Colosseum, the world’s largest network emulator, and tested in a variety of network conditions and scenarios. Our experimental results show that using QCell significantly improves user’s throughput (up to 37.6%) and the size of transmission queues (up to 11.9%), decreasing service latency.
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- Award ID(s):
- 1925601
- PAR ID:
- 10298727
- Date Published:
- Journal Name:
- IEEE Globecom 2021
- Page Range / eLocation ID:
- 1-6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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