A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT

Industrial Internet of Things (IIoT) networks will provide Ultra-Reliable Low-Latency Communication (URLLC) to support critical processes underlying the production chains. However, standard protocols for allocating wireless resources may not optimize the latency-reliability trade-off, especially for uplink communication. For example, centralized grant-based scheduling can ensure almost zero collisions, but introduces delays in the way resources are requested by the User Equipments (UEs) and granted by the gNB. In turn, distributed scheduling (e.g., based on random access), in which UEs autonomously choose the resources for transmission, may lead to potentially many collisions especially when the traffic increases. In this work we propose DIStributed combinatorial NEural linear Thompson Sampling (DISNETS), a novel scheduling framework that combines the best of the two worlds. By leveraging a feedback signal from the gNB and reinforcement learning, the UEs are trained to autonomously optimize their uplink transmissions by selecting the available resources to minimize the number of collisions, without additional message exchange to/from the gNB. DISNETS is a distributed, multi-agent adaptation of the Neural Linear Thompson Sampling (NLTS) algorithm, which has been further extended to admit multiple parallel actions. We demonstrate the superior performance of DISNETS in addressing URLLC in IIoT scenarios compared to other baselines

Adaptive Transmission Parameters Selection Algorithm for URLLC Traffic in Uplink

Ultra-Reliable Low-Latency Communications (URLLC) is a novel feature of 5G cellular systems. To satisfy strict URLLC requirements for uplink data transmission, the specifications of 5G systems introduce the grant-free channel access method. According to this method, a User Equipment (UE) performs packet transmission without requesting channel resources from a base station (gNB). With the grant-free channel access, the gNB configures the uplink transmission parameters in a long-term time scale. Since the channel quality can significantly change in time and frequency domains, the gNB should select robust transmission parameters to satisfy the URLLC requirements. Many existing studies consider fixed robust uplink transmission parameter selection that allows satisfying the requirements even for UEs with poor channel conditions. However, the more robust transmission parameters are selected, the lower is the network capacity. In this paper, we propose an adaptive algorithm that selects the transmission parameters depending on the channel quality based on the signal-to-noise ratio statistics analysis at the gNB. Simulation results obtained with NS-3 show that the algorithm allows meeting the URLLC latency and reliability requirements while reducing the channel resource consumption more than twice in comparison with the fixed transmission parameters selection.Comment: 7th International Conference "Engineering & Telecommunication - En&T-2020

Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach

This paper addresses the problem of enabling inter-machine Ultra-Reliable Low-Latency Communication (URLLC) in future 6G Industrial Internet of Things (IIoT) networks. As far as the Radio Access Network (RAN) is concerned, centralized pre-configured resource allocation requires scheduling grants to be disseminated to the User Equipments (UEs) before uplink transmissions, which is not efficient for URLLC, especially in case of flexible/unpredictable traffic. To alleviate this burden, we study a distributed, user-centric scheme based on machine learning in which UEs autonomously select their uplink radio resources without the need to wait for scheduling grants or preconfiguration of connections. Using simulation, we demonstrate that a Multi-Armed Bandit (MAB) approach represents a desirable solution to allocate resources with URLLC in mind in an IIoT environment, in case of both periodic and aperiodic traffic, even considering highly populated networks and aggressive traffic.Comment: 2022 IEEE Globecom Workshops (GC Wkshps): Future of Wireless Access and Sensing for Industrial IoT (FutureIIoT

Closed-form Approximation for Performance Bound of Finite Blocklength Massive MIMO Transmission

Ultra-reliable low latency communications (uRLLC) is adopted in the fifth generation (5G) mobile networks to better support mission-critical applications that demand high level of reliability and low latency. With the aid of well-established multiple-input multiple-output (MIMO) information theory, uRLLC in the future 6G is expected to provide enhanced capability towards extreme connectivity. Since the latency constraint can be represented equivalently by blocklength, channel coding theory at finite block-length plays an important role in the theoretic analysis of uRLLC. On the basis of Polyanskiy's and Yang's asymptotic results, we first derive the exact close-form expressions for the expectation and variance of channel dispersion. Then, the bound of average maximal achievable rate is given for massive MIMO systems in ideal independent and identically distributed fading channels. This is the study to reveal the underlying connections among the fundamental parameters in MIMO transmissions in a concise and complete close-form formula. Most importantly, the inversely proportional law observed therein implies that the latency can be further reduced at expense of spatial degrees of freedom