Federated-Learning-Enabled Intelligent Fog Radio Access Networks: Fundamental Theory, Key Techniques, and Future Trends

Zhao, Zhongyuan; Feng, Chenyuan; Yang, Howard H.; Luo, Xueting

doi:10.1109/mwc.001.1900370

Cited by 116 publications

(43 citation statements)

References 9 publications

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“…Determine EE-optimal and SE-optimal resource allocations, denoted by, (P SE , Q SE , a SE , b SE ) and (P EE , Q EE , a EE , b EE ), respectively, by using Algorithm 1 and Algorithm 2. 2: Initialize: (1)…”

Section: A Near-optimal Algorithm Designmentioning

confidence: 99%

“…Accordingly, by taking linear combination of the two consecutive points of F, we obtain F (1) as a new set of weakly Pareto optimal solutions to P0 where |F (1) | − |F| = N F − 1. In this way, the set of achievable weakly Pareto optimal solutions can be evolved as F → F (1) → F (2) → • • • → F (f inal) . In the aforementioned sequence, the number of weakly Pareto optimal solutions is increased at each evolution.…”

Section: Appendix Gmentioning

confidence: 99%

“…Leveraging centralized cloud processing based resource allocation, distributed signal processing, and popular content caching at the network edge, fog-radio access network (F-RAN) presents a revolutionary paradigm to satisfy the beyond 5G (B5G) performance requirements [1]. In edgecaching (EC) enabled F-RAN, the content distribution phase is significantly important to ensure throughput and diverse latency requirements of the user devices (UDs).…”

Section: Introductionmentioning

confidence: 99%

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Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

Hassan¹

2021

Preprint

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<div>Multi-objective resource allocation is studied for edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (SE) and interference management are investigated for distributing contents from the cache-enabled fog access points (F-APs) and cloud base station (CBS) to the user devices (UDs). In our envisioned system, the UDs are grouped into multiple non-overlapping device-clusters based on their locations. A rate-splitting with common message decoding based transmission strategy is applied to enable UDs of each device-cluster to receive data from a suitably selected F-AP and CBS over the same radio resource blocks. To maximize system EE and SE jointly, a multi-objective optimization problem (MOOP) is formulated and it is solved in three stages. At first, by employing the $\epsilon$-constraint method, the MOOP is converted to an EE-SE trade-off optimization problem. Then, by leveraging iterative function evaluation based power control and generalized 3D-resource matching, the EE-SE trade-off optimization problem is solved and a novel resource allocation algorithm is proposed to obtain near-optimal Pareto-front for the proposed MOOP. To reduce the complexity of obtaining near-optimal Pareto-front, a sub-optimal resource allocation algorithm is proposed as well. Finally, a low-complexity algorithm is devised to select a suitable operating EE-SE pair from the obtained Pareto-front. The conducted simulations demonstrate that the proposed resource allocation schemes achieve substantial improvement of system EE and SE over the benchmark schemes. </div>

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Section: A Near-optimal Algorithm Designmentioning

confidence: 99%

Section: Appendix Gmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

Hassan¹

2021

Preprint

View full text Add to dashboard Cite

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“…The deep reinforcement learning has also been applied to realize network slicing in F-RAN in [17], where computing, caching and radio resources are orchestrated to meet the performance requirements of two different types of services: hot-spot and vehicle-to-infrastructure (V2I). To address the challenges of high cost of data offloading and model training for implementing network intelligence at the edge, an evolved architecture of F-RAN is proposed in [18], which employs federated learning (a.k.a. collaborative learning) to realize intelligent signal processing and network management with less communication overhead and greater efficiency than existing centralized learning paradigms.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Opportunistic Fog Based Radio Access Networks

2020

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“…Leveraging distributed signal processing, resource allocation, and popular content caching at the network edge, fog-radio access network (F-RAN) presents a revolutionary paradigm to alleviate burden over fronthaul network in the beyond 5G architecture [1]. In a multi-level edgecaching (EC) enabled system, the content distribution phase is significantly important to ensure throughput and diverse latency requirements of the end-users.…”

Section: Introductionmentioning

confidence: 99%

Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

Hassan

Hossain

Cheng

et al. 2021

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Multi-objective resource allocations are studied for a multi-level edge-caching enabled fog-radio access network. Notably, joint maximization of the energy-efficiency (EE) and spectrum-efficiency (EE) and interference management for the content distribution phase are investigated. In the envisioned system, the popular contents are cached at both the fog access point (F-AP) and fog user equipments (F-UEs), and the content-requesting devices are grouped into multiple device-clusters based on their locations. Using a rate-splitting with common message decoding based transmission strategy, each device-cluster is simultaneously served by a suitably selected F-UE and the F-AP over the same radio resource blocks. To maximize system EE and SE of the content distribution phase jointly, a multi-objective optimization problem (MOOP) is formulated. By using the-constraint method, the proposed MOOP is converted to an EE-SE trade-off optimization problem. Leveraging iterative function evaluation based power control and generalized 3D-resource matching, a novel algorithm is devised to obtain near-optimal Pareto front for the proposed MOOP. By inspecting solutions over the Pareto optimal front, a suitable operating EE-SE pair and the corresponding resource allocation variables are obtained. A low-complexity algorithm to obtain a suitable EE-SE trade-off is also devised. The conducted simulations confirm that the proposed algorithms achieve substantial improvement of system EE and SE over the baseline schemes.

show abstract

Federated-Learning-Enabled Intelligent Fog Radio Access Networks: Fundamental Theory, Key Techniques, and Future Trends

Cited by 116 publications

References 9 publications

Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

Performance Analysis of Opportunistic Fog Based Radio Access Networks

Energy-Spectrum Efficient Content Distribution in Fog-RAN Using Rate-Splitting, Common Message Decoding, and 3D-Resource Matching

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