The management of a 5G system comprises Operation and Management aspects defined by 3GPP, including Network Slicing, and the Management and Orchestration aspects specified in ETSI’s Network Function Virtualization framework. Our Proof-of-Concept demonstrates the implementation of an on-demand provisioning procedure of a Network Slice Subnet composed of Virtual Network Functions from potentially different vendors. The demonstration includes a Network Management System conforming to the 3GPP Service-Based Management Architecture, an ETSI MANO orchestrator, and a Network Function Virtualization Infrastructure.

We study the problem of user association, namely finding the optimal assignment of user equipment to base stations to achieve a targeted network performance. In this paper, we focus on the knowledge transferability of association policies. Indeed, traditional non-trivial user association schemes are often scenario-specific or deployment-specific and require a policy re-design or re-learning when the number or the position of the users change. In contrast, transferability allows to apply a single user association policy, devised for a specific scenario, to other distinct user deployments, without needing a substantial re-learning or re-design phase and considerably reducing its computational and management complexity. To achieve transferability, we first cast user association as a multi-agent reinforcement learning problem. Then, based on a neural attention mechanism that we specifically conceived for this context, we propose a novel distributed policy network architecture, which is transferable among users with zero-shot generalization capability i.e., without requiring additional training. Numerical results show the effectiveness of our solution in terms of overall network communication rate, outperforming centralized benchmarks even when the number of users doubles with respect to the initial training point.

The Multi-Access Edge Computing (MEC) constitutes computing over virtualized resources distributed at the edge of mobile network. For mobile users, an optimal allocation of communication and computing resources changes over time and space, and the resource allocation becomes a complex problem. Moreover, for delay constrained applications, the resource allocation to mobile users cannot be solved by approaches designed for static users, as a solution would not be obtained within a desired time. Thus, in this paper, we propose a low-complexity computing and communication resource allocation for offloading of real-time computing tasks generated with a high arrival rate by the mobile users. We exploit probabilistic modeling of the users’ movement to pre-allocate the computing resources at base stations and to select suitable communication paths between the users and the base station with the pre-allocated computing resources. The simulations show that the proposed algorithm keeps the offloading delay below 100 ms for the small tasks even with the arrival rate of five tasks per second per user, while the state-of-the-art algorithms can handle only up to 0.5 tasks per second per user. Thus, the proposal enables an exploitation of the MEC for various real-time applications even if the users are moving.

This paper proposes a novel algorithmic solution for dynamic computation offloading, aimed at reducing the energy consumption of a mobile network endowed with multi-access edge computing. The dynamic evolution of the system is modeled through three queues: a local queue at the user side, a computation queue at the edge server, and a queue of results at the network access point. The optimization problem is cast as the minimization of the long-term average energy consumption of the whole system, comprising user devices, servers, and access points. Quality of service constraints for end users are imposed in terms of probability that the \textit{sum of the queues} exceeds a given threshold. A suitable weighting parameter can be tuned to drive the system toward a user-centric, a network-centric, or a hybrid solution. Exploiting stochastic optimization tools, the problem is solved thanks to a dynamic optimization algorithm, based on the solution of deterministic convex problems in each time slot. The algorithm does not assume any knowledge on the task input and output random sizes and the radio channel statistics. Several numerical results illustrate the advantages of the proposed method.

This paper deals with Low-Density Construction-A (LDA) lattices, which are obtained via Construction A from non-binary low-density parity-check codes. More precisely, a proof is provided that Voronoi constellations of LDA lattices achieve capacity of the AWGN channel under lattice encoding and decoding for every signal-to-noise ratio greater than 1. This is obtained after showing the same result for more general Construction-A lattice constellations. The theoretical analysis is carried out in a way that allows to describe how the prime number underlying Construction A behaves as a function of the lattice dimension. Moreover, no dithering is required in the transmission scheme, simplifying some previous solutions of the problem. Remarkably, capacity is achievable with LDA lattice codes whose parity-check matrices have constant row and column Hamming weights. Some expansion properties of random bipartite graphs constitute an extremely important tool for dealing with sparse matrices and allow to find a lower bound for the minimum Euclidean distance of LDA lattices in our ensemble.

The problem of communicating over the additive white Gaussian noise (AWGN) channel with lattice codes is addressed in this paper. Theoretically, Voronoi constellations have proved to yield very powerful lattice codes when the fine/coding lattice is AWGN-good and the coarse/shaping lattice has an optimal shaping gain. However, achieving Shannon capacity with these premises and practically implementable encoding algorithms is in general not an easy task. In this paper, a new way to encode and demap Construction-A Voronoi lattice codes is presented. As a meaningful application of this scheme, the second part of the paper is focused on Leech constellations of lowdensity Construction-A (LDA) lattices: LDA Voronoi lattice codes are presented whose numerically measured waterfall region is situated at less than 0.8 dB from Shannon capacity. These LDA lattice codes are based on dual-diagonal nonbinary low-density parity-check codes. With this choice, encoding, iterative decoding, and demapping have all linear complexity in the block length.

The recently discovered family of generalized low-density (GLD) lattices brings new mathematical challenges to coding theorists and practitioners. Given the excellent performance of integer GLD lattices in high dimensions and motivated by the simple lattice structure used for fast iterative decoding, this paper is a first attempt to analyze GLD lattices for asymptotically large dimensions. Firstly, we describe non-binary GLD codes and show their asymptotic goodness in terms of minimum Hamming distance. Secondly, we consider a GLD lattice ensemble built via Construction A from non-binary GLD codes, and analyze their goodness with respect to Poltyrev limit on the Gaussian channel. Finally, at large dimensions and using a large code alphabet, we prove that infinite GLD lattice constellations attain Poltyrev capacity limit under maximum likelihood decoding.

We propose the construction of a new family of lattice sphere packings. Given a small-dimensional lattice, we start by building a first lattice in a large dimension by the direct sum of the small lattice. Then, the coordinates of the first large lattice are permuted to yield a second large-dimensional lattice. Finally, our generalized low-density (GLD) lattice is the intersection of the first and the second lattice. We restrict our construction in this paper to integer lattices. GLD lattices are the result of mixing classical lattice theory with modern coding theory. They are potential candidates not only for channel coding as coded modulations, but also for physical-layer network coding and for secure digital communications.

We address the problem of transmission of information over the AWGN channel using lattices. In particular, we will deal with previously introduced LDA lattices which are obtained by Construction A from LDPC codes over the finite field Fp. We will show how to build a particular ensemble of LDA lattices related to bipartite graphs with good expansion properties. We investigate the quality of this family under lattice decoding and show that a random member in it can be reliably decoded for any value of the channel noise variance up to Poltyrev limit. Values of p and the parameters for which optimal performance is guaranteed under lattice decoding are in accordance with the optimal parameters found experimentally under iterative decoding.

We describe a new family of integer lattices built from construction A and non-binary LDPC codes. An iterative message-passing algorithm suitable for decoding in high dimensions is proposed. This family of lattices, referred to as LDA lattices, follows the recent transition of Euclidean codes from their classical theory to their modern approach as announced by the pioneering work of Loeliger (1997), Erez, Litsyn, and Zamir (2004-2005). Besides their excellent performance near the capacity limit, LDA lattice construction is conceptually simpler than previously proposed lattices based on multiple nested binary codes and LDA decoding is less complex than real-valued message passing.