This paper formulates an optimization problem to extract the best QoS parameters as well as the best H264 parameters embodied by the video bitrates in a context of a near real-time multi-agent system deployed within a public network. The principle is to interface H264 with the 5G QoS to construct measurable objective functions in terms of the latency and in terms of the rendering quality that reflect the requirements of an end-user using the said multi-agent system. Once the optimization problem is built, we make it a linear optimization problem that allows for many solvers, e.g., the simplex algorithm. Through numerical evaluations, we show examples of combined QoS requests for the multi-agent system that ensure the video streaming service to well operate regarding the end-user requirements.

In this paper, we focus on the problem of the phase noise management when working at high frequencies close to Sub-Thz or THz bands. We consider the exponential approximation of the phase noise autocorrelation to insert new pilots for the discovery of the said autocorrelation that is helpful for receiver processings such as the Wiener filtering. We consider full-pilots symbols and sparse-pilots symbols in a DFT-s-OFDM chain for which we describe the allocation parameters to ensure a fair discovery taking into account the impact on the spectral efficiency and on the discovery latency. We propose a simple procedure between any transceiver and any receiver to agree on the good pilot scheme parameters.

This paper proposes to model the radio propagation in a railway environment at 60GHz. A measurement campaign is conducted in this perspective, as part of the French ANR mmW4Rail project, to provide channel transfer functions. By assuming they are modeled as the 3GPP 5G NR channel model, the channel transfer functions are analyzed to result in Large-Scale Parameters (LSPs): the path loss, the Ricean factor, and the RMS delay spread. The LSPs are compared with the LSPs already stored as existing scenarios in the 3GPP documents, which helps conclude about the need for defining a new scenario for railway communications at 60GHz.

The Future Railway Mobile Communication System (FRMCS) faces challenges in rural areas due to poor link budgets for terminals at the cell edge. This leads to increased system failures at the application level. In this paper, we propose an application-oriented scheduler based on Coordinated Multi-Point (CoMP) to address this issue. Our scheduler is designed to leverage the predictability of train locations to estimate failure probabilities of terminals and improve radio resource allocation decisions. We conduct system-level simulations, taking into account application-level Quality of Service requirements, to compare the performance of traditional schedulers with our proposed approach. The results show that our CoMP scheduler effectively reduces system failures and outperforms traditional scheduling techniques in both CoMP and non-CoMP networks.

In this paper, we focus on the selection of a pilot pattern to track the phase noise in high frequency bands in a DFT-s-OFDM chain. By considering the Wiener filter at the receiver side to perform the tracking, we use the inner cost function of the said filter as the cost function for the pilot selection. To obtain this cost function, the phase noise autocorrelation is required. Therefore, we introduce a new mathematical approximation of the autocorrelation function of the practical 3GPP phase noise model. At first, this leads to an analytical expression of the Wiener filter coefficients. Then, the said coefficients allow us to obtain an analytical expression of the cost function. Thus, by means of this result, we are able to provide a pilot pattern that jointly satisfies a constraint on the pilot overhead and a constraint on the minimum performance of the Wiener filter.

Standard Markov decision process (MDP) and reinforcement learning algorithms optimize the policy with respect to the expected gain. We propose an algorithm which enables to optimize an alternative objective: the probability that the gain is greater than a given value. The algorithm can be seen as an extension of the value iteration algorithm. We also show how the proposed algorithm could be generalized to use neural networks, similarly to the deep Q learning extension of Q learning.

In the context of railway systems, the application performance can be very critical and the radio conditions not advantageous. Hence, the communication problem parameters include both a survival time stemming from the application layer and a channel error probability stemming from the PHY layer. This paper proposes to consider the framework of Markov Decision Process (MDP) to design a strategy for scheduling radio resources based on both application and PHY layer parameters. The MDP approach enables to obtain the optimal strategy via the value iteration algorithm. The performance of this algorithm can thus serve as a benchmark to assess lower complexity schedulers. We show numerical evaluations where we compare the value iteration algorithm with other schedulers, including one based on deep Q learning.

We consider a multi-agent system where agents compete for the access to the radio resource. By combining some application-level parameters, such as the resilience, with a knowledge of the radio environment, we propose a new way of modeling the scheduling problem as an optimization problem. We design accordingly a low-complexity solver. The performance are compared with state-of-the-art schedulers via simulations. The numerical results show that this application-oriented scheduler performs better than standard schedulers. As a result, it offers more space for the selection of the application-level parameters to reach any arbitrary performance.

This paper proposes a new pilot design for a joint compensation of the channel and the phase noise in sub-THz frequencies. Relying on the low-pass nature of the phase noise and on the local flatness of the channel, we construct a circular pilot pattern associated with an algorithm capable of removing data-to-pilot interference. This allows for an efficient compensation of the inter-carrier interference even for very high carrier frequencies. Through numerical evaluations, we show that our algorithm significantly outperforms the classical phase noise tracking algorithms that rely on the 3GPP pilot design.

Allowing for asynchronous access and very high carrier frequencies, 5G presents a major challenge for OFDM-based waveforms not originally designed to deal with these cases. In this paper, we present the benefits of Static Sequence Assisted DFT-spread OFDM (SS-A DFT-s-OFDM) waveform for 5G networks. Through theoretical arguments and numerical results, we show that SS-A DFT-s-OFDM exhibits good properties to be competitive regarding the out-of-band emissions. SS-A DFT-s-OFDM also offers support for tracking of phase noise which becomes a problem in high frequency. The spectral efficiency is then improved with respect to the conventional DFT-s-OFDM making SS-A DFT-s-OFDM an attractive candidate for 5G.

Generalized belief propagation (GBP) is known to be a well-suited technique for approximate inference problems in loopy factor graphs. It can absorb problematic subgraphs inside regions to reduce their influence on the inference. However, the choice of regions to be used in GBP remains a delicate issue. This paper proposes an approach to create specific regions when dealing with Low-Density Parity-Check (LDPC) codes. We split trapping sets, known to degrade the decoding performance, to make GBP locally optimal. Experiments show that GBP can then perform better than BP, especially in the error-floor region.

In this paper, we focus on the behavior of the Belief Propagation (BP) algorithm when decoding the Low-Density Parity-Check (LDPC) code of rate 3/4 in the DVB-S2 standard. By studying the topological structure of its Tanner graph, we raise properties inherent to the degree distribution that turns out to be strongly correlated with the decoding failures. The irregularity of the degrees seriously damages the performance, visible by an abrupt error floor. We accordingly propose a novel model of the error events based on the degree distribution which helps simulate typical error events and observing the flaws of the DVB-S2 code. This work could be used as a good basis to design future codes with a better decoding behavior, especially in the error floor region.

The DVB-S2 standard has been revealed a reliable standard for data transmission thanks to the LDPC codes. The waterfall of the bit-error rate is very abrupt to reach low values of order 10^(-10) in the error floor. In this paper, we propose a novel investigation on the iterative decoding of these codes by the Belief Propagation algorithm on Gaussian channels. Introducing definitions of persistent and created errors, we analyze them according to the degrees of the Tanner graph nodes and the noise power of the channel. We show that the two-degree bits used to accelerate the encoding process of the DVB-S2 are responsible for most decoding errors. This result could help design DVB-S2 codes in a more appropriate way, for instance avoiding the use of BCH codes to tackle the error floor.