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Edge computing has been recently introduced as a way to bring computational capabilities closer to end users of modern network-based services, in order to support existent and future delay-sensitive applications by effectively addressing the high propagation delay issue that affects cloud computing. However, the problem of efficiently and fairly manage the system resources presents particular challenges due to the limited capacity of both edge nodes and wireless access networks, as well as the heterogeneity of resources and services' requirements. To this end, we propose a techno-economic market where service providers act as buyers, securing both radio and computing resources for the execution of their associated end users' jobs, while being constrained by a budget limit. We design an allocation mechanism that employs convex programming in order to find the unique market equilibrium point that maximizes fairness, while making sure that all buyers receive their preferred resource bundle. Additionally, we derive theoretical properties that confirm how the market equilibrium approach strikes a balance between fairness and efficiency. We also propose alternative allocation mechanisms and give a comparison with the market-based mechanism. Finally, we conduct simulations in order to numerically analyze and compare the performance of the mechanisms and confirm the theoretical properties of the market model., Comment: Corrected typos and figure orientation. in IEEE Transactions on Mobile Computing

Millimeter-wave (mm-wave) technologies are the main driver to deliver the multiple-Gbps promise in next-generation wireless access networks. However, the GHz-bandwidth potential must coexist with a harsh propagation environment. While strong attenuations can be compensated by directional antenna arrays, the severe impact of obstacle blockages can only be mitigated by smart resource allocation techniques. Multi-connectivity, as multiple mm-wave links from a mobile device to different base stations, is one of them. However, the higher reliability provided by several access alternatives can be fully exploited only if uncorrelated link statuses are guaranteed. Therefore, spatial diversity must be enforced. Moreover, since interposing obstacles can block a link, short access links allow reducing the link unavailability probability. Smart base-station selections can be made once the network is deployed, however, our results show that much better results are achievable if spatial diversity and link-length aspects are directly included in the network planning phase. In this article, we propose an mm-wave access network planning framework that considers base-station spatial diversity, link lengths, and achievable user throughput, according to channel conditions and network congestion. The comparison against traditional k-coverage approaches shows that our approach can obtain much better access reliability, thus providing higher robustness to random obstacles and self-blockage phenomena.

Millimeter wave (mmWave) communications have been introduced in the 5G standardization process due to their attractive potential to provide a huge capacity extension to traditional sub-6 GHz technologies. However, such high-frequency communications are characterized by harsh propagation conditions, thus requiring base stations to be densely deployed. Integrated access and backhaul (IAB) network architecture proposed by 3GPP is gaining momentum as the most promising and cost-effective solution to this need of network densification. IAB networks’ available resources need to be carefully tuned in a complex setting, including directional transmissions, device heterogeneity, and intermittent links with different levels of availability that quickly change over time. It is hard for traditional optimization techniques to provide alone the best performance in these conditions. We believe that Deep Reinforcement Learning (DRL) techniques, especially assisted with Long Short-Term Memory (LSTM), can implicitly capture the regularities of environment dynamics and learn the best resource allocation strategy in networks affected by obstacle blockages. In this article, we propose a DRL based framework based on the Column Generation (CG) that shows remarkable effectiveness in addressing routing and link scheduling in mmWawe 5G IAB networks in realistic scenarios.