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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2510.26135 |
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| _version_ | 1866908619942920192 |
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| author | Yu, Tao Wang, Simin Zhang, Shunqing Cui, Mingyao Huang, Kaibin Chen, Wen Wu, QingQing Li, Jihong Huang, Kaixuan |
| author_facet | Yu, Tao Wang, Simin Zhang, Shunqing Cui, Mingyao Huang, Kaibin Chen, Wen Wu, QingQing Li, Jihong Huang, Kaixuan |
| contents | The imminent emergence of sixth-generation (6G) networks faces critical challenges from spatially heterogeneous traffic and escalating energy consumption, necessitating sustainable scaling strategies for network infrastructure such as base stations (BSs) and reconfigurable intelligent surfaces (RISs). This paper establishes fundamental scaling laws for the Integrated Relative Energy Efficiency (IREE) metric under joint multi-BS and multi-RIS deployment in traffic-mismatched scenarios. Specifically, we propose an Alternating Directional Dual-Radial Basis Function (ADD-RBF) framework that models the channels of BSs and RISs as two type of spatially decoupled RBF neurons to maximize IREE through alternative optimization, with proven universal approximation capability and convergence guarantees. Theoretical analysis reveals a scaling dichotomy: BS proliferation drives logarithmic capacity growth $\mathcal{O}(\log N^{BS})$ but only polynomial mismatch reduction $\mathcal{O}(1/\sqrt{N^{BS}})$, whereas RIS deployment achieves exponential mismatch mitigation $\mathcal{O}(δ_{\text{err}}^{-(N^R+1)})$ despite its sub-logarithmic capacity gains. Simulation results validate that RISs excel in capturing spatial traffic correlations and alleviating hotspots, making them particularly effective when mismatch dominates, while BSs are preferable under capacity shortages. These findings offer practical guidelines for green 6G network design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26135 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Green Wireless Network Scaling for Joint Deployment: Multi-BSs or Multi-RISs? Yu, Tao Wang, Simin Zhang, Shunqing Cui, Mingyao Huang, Kaibin Chen, Wen Wu, QingQing Li, Jihong Huang, Kaixuan Systems and Control The imminent emergence of sixth-generation (6G) networks faces critical challenges from spatially heterogeneous traffic and escalating energy consumption, necessitating sustainable scaling strategies for network infrastructure such as base stations (BSs) and reconfigurable intelligent surfaces (RISs). This paper establishes fundamental scaling laws for the Integrated Relative Energy Efficiency (IREE) metric under joint multi-BS and multi-RIS deployment in traffic-mismatched scenarios. Specifically, we propose an Alternating Directional Dual-Radial Basis Function (ADD-RBF) framework that models the channels of BSs and RISs as two type of spatially decoupled RBF neurons to maximize IREE through alternative optimization, with proven universal approximation capability and convergence guarantees. Theoretical analysis reveals a scaling dichotomy: BS proliferation drives logarithmic capacity growth $\mathcal{O}(\log N^{BS})$ but only polynomial mismatch reduction $\mathcal{O}(1/\sqrt{N^{BS}})$, whereas RIS deployment achieves exponential mismatch mitigation $\mathcal{O}(δ_{\text{err}}^{-(N^R+1)})$ despite its sub-logarithmic capacity gains. Simulation results validate that RISs excel in capturing spatial traffic correlations and alleviating hotspots, making them particularly effective when mismatch dominates, while BSs are preferable under capacity shortages. These findings offer practical guidelines for green 6G network design. |
| title | Green Wireless Network Scaling for Joint Deployment: Multi-BSs or Multi-RISs? |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.26135 |