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Massive MIMO is a leading technology to connect 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. We here 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 realise a simple partitioning method requiring minimal information to be collected from the nodes, and where this information typically remains stable over a long term, thus promoting their autonomy and energy efficiency.

In this paper we address the problem of resource allocation for alarm traffic in industrial Internet of Things networks using massive MIMO. We formulate the general problem of how to allocate pilot signals to alarm traffic such that delivery is guaranteed, while also minimising the number of pilots reserved for alarms, thus maximising the channel resources available for other traffic, such as industrial control traffic. We present an algorithm that fulfils these requirements, and evaluate its performance both analytically and through a simulation study. For realistic alarm traffic characteristics, on average our algorithm can deliver alarms within two time slots (of duration equal to the 5G transmission time interval) using fewer than 1.5 pilots per slot, and even in the worst case it uses around 3.5 pilots in any given slot, with delivery guaranteed in an average of approximately four slots., Comment: 11th International Workshop on Resilient Networks Design and Modeling (RNDM) 2019

In this paper we present new optimization formulations for maximizing the network lifetime in wireless mesh networks performing data aggregation and dissemination for machine-to-machine communication in the Internet of Things. We focus on heterogeneous networks in which multiple applications co-exist and nodes may take on different roles for different applications. Moreover, we address network reconfiguration as a means to increase the network lifetime, in keeping with the current trend towards software defined networks and network function virtualization. To test our optimization formulations, we conducted a numerical study using randomly-generated mesh networks from 10 to 30 nodes, and showed that the network lifetime can be increased using network reconfiguration by up to 75% over a single, minimal-energy configuration. Further, our solutions are feasible to implement in practical scenarios: only few configurations are needed, thus requiring little storage for a standalone network, and the synchronization and signalling needed to switch configurations is low relative to each configuration's operating time., Comment: Ad Hoc Networks, in press

Massive multiple-input multiple-output (MIMO) is expected to be a vital component in future 5G systems. As such, there is a need for new modeling in order to investigate the performance of massive MIMO not only at the physical layer, but also higher up the networking stack. In this paper, we present general optimization models for massive MIMO, based on mixed-integer programming and compatible sets, with both maximum ratio combing and zero forcing precoding schemes. We then apply our models to the case of joint device scheduling and power control for heterogeneous devices and traffic demands, in contrast to existing power control schemes that consider only homogeneous users and saturated scenarios. Our results show substantial benefits in terms of energy usage can be achieved without sacrificing throughput, and that both signalling overhead and the complexity of end devices can be reduced by abrogating the need for uplink power control through efficient scheduling.

The Internet of Things (IoT) paradigm brings an opportunity for advanced Demand Response (DR) solutions. It enables visibility and control on the various appliances that may consume, store or generate energy within a home. It has been shown that a centralized control on the appliances of a set of households leads to efficient DR mechanisms; unfortunately, such solutions raise privacy and scalability issues. In this chapter we propose an approach that deals with these issues. Specifically, we introduce a scalable two-levels control system where a centralized controller allocates power to each house on one side and, each household implements a DR local solution on the other side. A limited feedback to the centralized controller allows to enhance the performance with little impact on privacy. The solution is proposed for the general framework of capacity markets.

With densification of nodes in cellular networks, free space optic (FSO) connections are becoming an appealing low cost and high rate alternative to copper and fiber as the backhaul solution for wireless communication systems. To ensure a reliable cellular backhaul, provisions for redundant, disjoint paths between the nodes must be made in the design phase. This paper aims at finding a cost-effective solution to upgrade the cellular backhaul with pre-deployed optical fibers using FSO links and mirror components. Since the quality of the FSO links depends on several factors, such as transmission distance, power, and weather conditions, we adopt an elaborate formulation to calculate link reliability. We present a novel integer linear programming model to approach optimal FSO backhaul design, guaranteeing $K$-disjoint paths connecting each node pair. Next, we derive a column generation method to a path-oriented mathematical formulation. Applying the method in a sequential manner enables high computational scalability. We use realistic scenarios to demonstrate our approaches efficiently provide optimal or near-optimal solutions, and thereby allow for accurately dealing with the trade-off between cost and reliability.