Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today's mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station to serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing Key Performance Indicators (KPIs) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing the average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks, but also exhibits significantly more reliable performance, e.g., 71.4% less variance in latency.

Wireless backhauling at millimeter-wave frequencies (mmWave) in static scenarios is a well-established practice in cellular networks. However, highly directional and adaptive beamforming in today's mmWave systems have opened new possibilities for self-backhauling. Tapping into this potential, 3GPP has standardized Integrated Access and Backhaul (IAB) allowing the same base station to serve both access and backhaul traffic. Although much more cost-effective and flexible, resource allocation and path selection in IAB mmWave networks is a formidable task. To date, prior works have addressed this challenge through a plethora of classic optimization and learning methods, generally optimizing a Key Performance Indicator (KPI) such as throughput, latency, and fairness, and little attention has been paid to the reliability of the KPI. We propose Safehaul, a risk-averse learning-based solution for IAB mmWave networks. In addition to optimizing average performance, Safehaul ensures reliability by minimizing the losses in the tail of the performance distribution. We develop a novel simulator and show via extensive simulations that Safehaul not only reduces the latency by up to 43.2% compared to the benchmarks, but also exhibits significantly more reliable performance, e.g., 71.4% less variance in achieved latency.

Millimeter-wave (mmWave) backhauling is key to ultra-dense deployments in beyond-5G networks because providing every base station with a dedicated fiber-optic backhaul link to the core network is technically too complicated and economically too costly. Self-backhauling allows the operators to provide fiber connectivity only to a small subset of base stations (Fiber-BSs), whereas the rest of the base stations reach the core network via a (multi-hop) wireless link towards the Fiber-BS. Although a very attractive architecture, self-backhauling is proven to be an NP-hard route selection and resource allocation problem. The existing self-backhauling solutions lack practicality because: (i) they require solving a fairly complex combinatorial problem every time there is a change in the network (e.g., channel fluctuations), or (ii) they ignore the impact of network dynamics which are inherent to mobile networks. In this article, we propose SCAROS which is a semi-distributed learning algorithm that aims at minimizing the end-to-end latency as well as enhancing the robustness against network dynamics including load imbalance, channel variations, and link failures. We benchmark SCAROS against state-of-the-art approaches under a real-world deployment scenario in Manhattan and using realistic beam patterns obtained from off-the-shelf mmWave devices. The evaluation demonstrates that SCAROS achieves the lowest latency, at least 1.8 × higher throughput, and the highest flexibility against variability or link failures in the system.

The complexity of the mode selection and resource allocation (MSRA) problem has hampered the commercialization progress of Device-to-Device (D2D) communication in 5G networks. Furthermore, the combinatorial nature of MSRA has forced the majority of existing proposals to focus on constrained scenarios or offline solutions to contain the size of the problem. Given the real-time constraints in actual deployments, a reduction in computational complexity is necessary. Adaptability is another key requirement for mobile networks that are exposed to constant changes such as channel quality fluctuations and mobility. In this article, we propose an online learning technique (i.e., CBMoS) which leverages combinatorial multi-armed bandits (CMAB) to tackle the combinatorial nature of MSRA. Furthermore, our two-stage CMAB design results in a tight model, which eliminates the theoretically feasible but practicality invalid options from the solution space. We prototype the first SDR-based D2D testbed to verify the performance of CBMoS under real-world conditions. The simulations confirm that the fast learning speed of CBMoS leads to outperforming the benchmark schemes by up to 132%. In experiments, CBMoS exhibits even higher performance (up to 142%) than in the simulations. This stems from the adaptability/fast learning speed of CBMoS in presence of high channel dynamics which cannot be captured via statistical channel models used in the simulators.