A hierarchical AI-based control plane solution for multitechnology deterministic networks

Following the Industry 4.0 vision of a full digitiSation of the industry, time-critical services and applications, allowing network infrastructures to deliver information with determinism and reliability, are becoming more and more relevant for a set of vertical sectors. As a consequence, deterministic network solutions are progressively emerging, albeit they are still bounded to specific technological domains. Even considering the existence of interconnected deterministic networks, the provision of an end-to-end (E2E) deterministic service over them must rely on a specific control plane architecture, capable of seamlessly integrate and control the underlying multi-technology data plane. In this work, we envision such a control plane solution, extending previous works and exploiting several innovations and novel architectural concepts. The proposed control architecture is service-centric, in order to provide the necessary flexibility, scalability, and modularity to deal with a heterogenous data plane. The architecture is hierarchical and encompasses a set of management platforms to interact with specific network technologies overarched by an E2E platform for the management, monitoring, and control of E2E deterministic services. Furthermore, Artificial Intelligence (AI) and Digital Twinning are used to enable network predictability and automation, as well as smart resource allocation, to ensure service reliability in dynamic scenarios where existing services may terminate and new ones may need to be deployed

A 38pJ/b Optimal Soft-MIMO Detector

An optimal soft multiple-input multiple-output (MIMO) detector is proposed with linear complexity for a general spatial multiplexing system with two transmitting symbols and NR ≥ 2 receiving antennas. The computational complexity of the proposed scheme is independent of the operating signal-tonoise ratio (SNR) and grows linearly with the constellation order. It provides the soft maximum-likelihood (ML) solution using an efficient Log-Likelihood Ratio (LLR) calculation method, avoiding the exhaustive search on all the candidate nodes. Moreover, an efficient pipelined hardware implementation of the detection algorithm is proposed, which is fabricated and fully tested in a 130nm CMOS technology. Operating at 1.2 V supply with 412 MHz clock, the chip achieves up to 5 Gbps throughput with 192mW power dissipation and an energy efficiency of 38 pJ/b, showing a great potential to be used in next generation Gbps wireless systems. The proposed MIMO detector is perfectly suitable to be applied to the Long Term Evolution (LTE) modem as well as Wi-Fi and WiGig devices with more than 1 RF chain. Synthesis results in a 90nm CMOS technology demonstrates that the design can operate at a sustained throughput of 6.2 Gbps, and an energy efficiency of 28 pJ/b at 1.2 V supply. For applications demanding a lower throughput regime, the core can operate at 0.9 V supply consuming 42mW providing a throughput of 1 Gbps1

A hierarchical AI-based control plane solution for multi-technology deterministic networks

Following the Industry 4.0 vision of a full digitization of the industry, time-critical services and applications, allowing network infrastructures to deliver information with determinism and reliability, are becoming more and more relevant for a set of vertical sectors. As a consequence, deterministic network solutions are progressively emerging, albeit they are still bounded to specific technological domains. Even considering the existence of interconnected deterministic networks, the provision of an end-to- end (E2E) deterministic service over them must rely on a specific control plane architecture, capable of seamlessly integrate and control the underlying multi-technology data plane. In this work, we envision such a control plane solution, extending previous works and exploiting several innovations and novel architectural concepts. The proposed control architecture is service-centric, in order to provide the necessary flexibility, scalability, and modularity to deal with a heterogenous data plane. The architecture is hierarchical and encompasses a set of management platforms to interact with specific network technologies overarched by an E2E platform for the management, monitoring, and control of E2E deterministic services. Furthermore, Artificial Intelligence (AI) and Digital Twinning are used to enable network predictability and automation, as well as smart resource allocation, to ensure service reliability in dynamic scenarios where existing services may terminate and new ones may need to be deployed