In this paper, we tackle the problem of multi-connectivity for latency critical industrial IoT services in 5G and Beyond. Sending multiple copies of the same packet on different radio interfaces is a popular mean for reducing delay. However, systematic redundancy may lead to congestion and more dynamic scheduling schemes such as Join the Shortest Queue (JSQ) are not guaranteed to be optimal. In order to derive the optimal multi-connectivity scheme, we first develop a novel discrete-time queuing model for the system, that integrates the heterogeneity of radio conditions and can consider any multi-connectivity policy. We then formulate an associated Markov Decision Process (MDP) that learns, online, the optimal state/action combination. Our simulations demonstrate that the derived optimal scheme outperforms classical ones like systematic redundancy and JSQ and is able to adapt dynamically to different traffic conditions. Interestingly, our proposed scheme yields a clear advantage when the objective is to ensure a hard latency constraint compared with the case where the average delay is to be minimized.

Network slicing has emerged as a promising technical solution to ensure the coexistence of the various 5G services. While the evolution of the 5G architecture for supporting slicing has been thoroughly studied, the impact of the architectural options on RAN resource allocation efficiency is still unclear. This article fills a gap in this area by assessing the impact of architecture choices on the quality of service of different services, with a focus on ultra-reliable low-latency communication applications. We propose architectural options based on the placement of the entities responsible for implementing these functions. We then assess their impact on the radio resource allocation flexibility when slices span two radio access technologies with redundant coverage. Our numerical experiments show that the placement of the slice management functions plays a crucial role in the choice of the radio resource allocation scheme that best fits URLLC slices.

In this work, we develop tractable flow level performance models for data traffic in cellular networks when users are moving inside the cell and among cells. Due to mobility radio conditions may change during transfer and classical flow level models can no more be used. We first consider an exact Markovian model that models explicitly the mobility between zones of different radio conditions during the data transfer and derive performance metrics such as sojourn and downloading times. However, as this Markovian model does not scale with the number of radio conditions and the number of admissible users, we propose a closed-form approximation model, based on a multi-class Processor Sharing. Numerical results show that the proposed model provides a very tight approximation. We then use this model in order to study the impact of mobility on different performance metrics.

In this work, we combine base station sleep mode strategies and device to device (D2D) communications to design solutions for 5G green mobile networks. The basic idea is that switching-off base stations allows to save energy while D2D technology guarantees coverage. We consider the realistic case of multi-layer cellular network and explore two sleep mode mechanisms: partial sleep mode (PSM) where only the frequency layer guaranteeing coverage at cell-edge is turned off, and deep sleep mode (DSM) where the base station is switched-off completely. Once a base station is switched-off multi-hop D2D communications are enabled to provide coverage. Cellular and D2D communications are modeled using queuing theory and sharing radio resources among them in an optimal way guaranteeing the stability of the system. We evaluate the time period of sleeping base stations in function of device battery discharging and hence the corresponding network energy savings. Results show that the combination of sleep mode strategies and D2D communications provides energy savings using an acceptable device battery discharge.

Today, mobile networks are witnessing an exponential growth of traffic volumes, linked to new services, especially for smart cities and smart-grid. The European Celtic-Plus SooGREEN project, started mid 2015, is targeting to reduce the energy consumption of the services in different mobile architectures in interaction with smart-grid. So GREEN is focused on the services energy consumption modelling and measurement, the dynamic optimization of the mobile access network and of the content delivery, the design of an Energy Efficient Virtualized and Centralized Radio Access Network (RAN), and the bi-directional interaction of the mobile network with the smart-grid. This paper presents insight into the project after its first year, and discusses research trends in green communication networks for the future.

This paper investigates the network offloading capability of Device to Device (D2D) communication when combined with content caching. We consider a mobile network where a proportion of users are equipped with caches from which other users can retrieve contents using direct D2D communications. In this case, the offloading capacity depends both on the cache performance and the quality of the D2D links. As of the cache performance, we develop an analytical model for the cache hit rate for different content prefetching strategies. We then inject this hit rate in a queuing theory analysis of the network capacity where the macro base station and the D2D links are modeled as coupled Processor Sharing (PS) queues. We develop closed-form solutions for the stability region and the performance gains for both one-hop and two-hop D2D. Our simulation results illustrate the offloading capacity and present optimal cache and network parameters that maximize the benefit of D2D communications.

The demonstration presents a network coding broadcast protocol experiment running on a remote open testbed, IoT-LAB. The emphasis is on both parts, that are ideally fitting and complementary: the use of testbed IoT-LAB, and a protocol for broadcast with network coding. IoT-LAB is a very large scale testbed, remotely accessible, and includes a total of 2728 nodes (in 6 sites), the nodes are mostly of type "wireless sensor nodes" with one wireless radio transceiver, and are well suited to perform wireless protocol experiments. On the other hand, network coding is a technique perfectly fitted to multi-hop wireless networks with lossy links, in some cases and conditions, it may even outperform any scheme using routing (non-coding): we had designed a generic broadcast protocol, called DRAGONCAST based on network coding for such networks, and which minimizes the assumptions made of the networks. A variant of this protocol was run on IoT-LAB: some results were presented previously. The demonstration is a live demonstration of the protocol on the newer nodes of IoT-LAB.

In this paper, we analyze the optimal blockwise subcarrier allocation scheme in coded Orthogonal Frequency Division Multiple Access (OFDMA) uplink systems without channel state information at the transmitter side. Due to the Discrete Fourier Transform, the subcarrier gains in OFDMA systems are correlated and this correlation plays a determinant role since it causes a degradation of the transmission performance. As a consequence, there exist some subcarrier allocation schemes which are preferable to achieve higher diversity gains. We propose the minimization of an inter-carrier sum-correlation function defined as the sum of correlations of each subcarrier with respect to the others in the same allocation scheme. We show the robustness of the proposed blockwise allocation policy to the effect of carrier frequency offsets (CFO). Moreover, we propose the block-size which provides the best trade-off between diversity and CFO for practical values of CFO. Finally, numerical simulations validating our results are presented.

In this paper, we design a low complexity linear MMSE decoder to recover the signal transmitted by M mobile users to a base station equipped with N receiving antennas, arranged as a uniform linear array (ULA). The angles of arrival are supposed to be uniformly distributed. As the dimension increases, it is hard to invert the system and therefore recent results on random matrix theory and polynomial expansion detectors are helpful to approximate the optimum linear MMSE receiver. Simulation results confirm the validity of this approximation.

In this paper, we derive the explicit series expansion of the eigenvalue distribution of various models, namely the case of noncentral Wishart distributions, as well as correlated zero mean Wishart distributions. The tools used extend those of the free probability framework, which have been quite successful for high dimensional statistical inference (when the size of the matrices tends to infinity), also known as free deconvolution. This contribution focuses on the finite Gaussian case and proposes algorithmic methods to compute the moments. Cases where asymptotic results fail to apply are also discussed.