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Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular, massive multiple-input-multiple-output (MIMO) and its future evolution of large intelligent surfaces (LIS), which provide more reliable channel quality than previous technologies. At the same time, network slicing in 5G and beyond systems provides easier management of different categories of users and traffic, and a better basis for providing quality of service, especially for demanding use cases such as industrial control. In previous works, we have presented solutions for scheduling industrial control traffic in LIS and massive MIMO systems. We now consider the case of dynamic slicing in the radio access network, where we need to not only meet the stringent latency and reliability requirements of industrial control traffic, but also minimize the radio resources occupied by the network slice serving the control traffic, ensuring resources are available for lower-priority traffic slices. In this paper, we provide mixed-integer programming optimization formulations for radio resource usage minimization for dynamic network slicing. We tested our formulations in numerical experiments with varying traffic profiles and numbers of nodes, up to a maximum of 32 nodes. For all problem instances tested, we were able to calculate an optimal schedule within 1 s, making our approach feasible for use in real deployment scenarios. [ABSTRACT FROM AUTHOR]

Massive MIMO is one of the leading technologies for connecting very large numbers of energy-constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes into groups that can communicate simultaneously such that the mutual interference is minimized. Here we propose node partitioning strategies that do not require full channel state information, but rather are based on nodes' respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realize a simple partitioning method, requiring minimal information to be collected from the nodes, and in which this information typically remains stable over the long term, thus promoting the system's autonomy and energy efficiency. [ABSTRACT FROM AUTHOR]

We investigate the trade-off between energy usage and (packet) throughput in wireless mesh networks performing machine-to-machine communication. For this we provide a novel mixed-integer programming formulation to maximise the throughput while maintaining minimal energy usage, together with an effective price-and-branch solution algorithm based on column generation. The resulting optimisation model is the main original contribution of the presented paper. We conducted a numerical study using network examples from 10 to 40 nodes, in which periodic sensor measurements are aggregated and disseminated to actuators. In almost all cases, we were able to achieve maximum throughput and minimum energy usage simultaneously, and in those cases where this was not possible, the costs incurred in one objective in order to achieve the other were typically low. The solution times for all network sizes were of the order of seconds, showing that our optimisation model is feasible to use in practice. [ABSTRACT FROM AUTHOR]