Cell-free network is a new paradigm, originating from distributed MIMO, that has been investigated for a few recent years as an alternative to the celebrated cellular structure. Future networks not only consider classical data transmission but also positioning, along the lines of Integrated Communications and Sensing (ISAC). The goal of this paper is to investigate at the same time the ambiguity function which is an important metric for positioning and the understanding of its associated resolution and ambiguities, and the array gain when maximum ratio transmission (MRT) or MR combining (MRC) is implemented for data communications. In particular, the role and impact of using a waveform with non-zero bandwidth is investigated. The theoretical findings are illustrated by means of computational results.

Federated learning is envisioned to be a key enabler of network functionalities based on artificial intelligence. Multiple access mechanisms supporting the learning task must then be designed, in order to provide an efficient interplay between the communication and computation resources. This work considers thus a multi-level slotted random access scheme autonomously optimised by each node. Due to their mutual coupling, the nodes’ interaction is an instance of the Best Response Dynamics (BRD) of a Generalised Nash Equilibrium Problem (GNEP). Within this framework, levers are identified, guaranteeing the convergence of the interactions to an equilibrium point at which the federated learning task is supported. These levers, on which the network manager can act, are validated by numerical simulations. These latter moreover show that the performance loss due to the autonomous character of the nodes is negligible with respect to the result of a centralised optimisation. On a broader mathematical level, this work defines a class of GNEPs for which sufficient convergence conditions for the totally asynchronous BRD are obtained. The considered class, named the GNEPs with polyhedral strategy sets and variable right-hand sides, encompasses a wide variety of GNEPs, and in particular GNEPs which are neither jointly convex nor generalised potential games. The obtained conditions depend on the first and second derivatives of the objective and constraint functions, and they constitute thus an off-the-shelf framework to study the BRD of GNEPs belonging to the identified class.

We propose a novel multipath multi-hop adaptive and causal random linear network coding (AC-RLNC) algorithm with forward error correction. This algorithm generalizes our joint optimization coding solution for point-to-point communication with delayed feedback. AC-RLNC is adaptive to the estimated channel condition, and is causal, as the coding adjusts the retransmission rates using a priori and posteriori algorithms. In the multipath network, to achieve the desired throughput and delay, we propose to incorporate an adaptive packet allocation algorithm for retransmission, across the available resources of the paths. This approach is based on a discrete water filling algorithm, i.e., bit-filling, but, with two desired objectives, maximize throughput and minimize the delay. In the multipath multi-hop setting, we propose a new decentralized balancing optimization algorithm. This balancing algorithm minimizes the throughput degradation, caused by the variations in the channel quality of the paths at each hop. Furthermore, to increase the efficiency, in terms of the desired objectives, we propose a new selective recoding method at the intermediate nodes. We derive bounds on the throughput and the mean and maximum in-order delivery delay of AC-RLNC, both in the multipath and multipath multi-hop case. In the multipath case, we prove that in the non-asymptotic regime, the suggested code may achieve more than 90% of the channel capacity with zero error probability under mean and maximum in-order delay constraints, namely a mean delay smaller than three times the optimal genie-aided one and a maximum delay within eight times the optimum. In the multipath multi-hop case, the balancing procedure is proven to be optimal with regards to the achieved rate. Through simulations, we demonstrate that the performance of our adaptive and causal approach, compared to selective repeat (SR)-ARQ protocol, is capable of gains up to a factor two in throughput and a factor of more than three in mean delay and eight in maximum delay. The improvements on the throughput delay trade-off are also shown to be significant with regards to the previously developed singlepath AC-RLNC solution.