Study of coded ALOHA with multi-user detection under heavy-tailed and correlated arrivals

In this paper, we study via simulation the performance of irregular repetition slotted ALOHA under multi-packet detection and different patterns of the load process. On the one hand, we model the arrival process with a version of the M/G/∞ process able to exhibit a correlation structure decaying slowly in time. Given the independence among frames in frame-synchronous coded-slotted ALOHA (CSA), this variation should only take effect on frame-asynchronous CSA. On the other hand, we vary the marginal distribution of the arrival process using discrete versions of the Lognormal and Pareto distributions, with the objective of investigating the influence of the right tail. In this case, both techniques should be affected by the change, albeit to a different degree. Our results confirm these hypotheses and show that these factors must be taken into account when designing and analyzing these systems. In frameless operations, both the shape of the packet arrivals tail distribution and the existence of short-range and long-range correlations strongly impact the packet loss ratio and the average delay. Nevertheless, these effects emerge only weakly in the case of frame-aligned operations, because this enforces the system to introduce a delay in the newly arrived packets (until the beginning of the next frame), and implies that the backlog of accumulated packets is the key quantity for calculating the performance.Ministerio de Ciencia e Innovación | Ref. PID2020-113240RB-I00Ministerio de Ciencia e Innovación | Ref. PID2020-113795RB-C3

On the Performance of Irregular Repetition Slotted ALOHA with an Age of Information Threshold

The present paper focuses on an IoT setting in which a large number of devices generate time-stamped updates addressed to a common gateway. Medium access is regulated following a grant-free approach, and the system aims at maintaining an up-to-date knowledge at the receiver, measured through the average network age of information (AoI). In this context, we propose a variation of the irregular repetition slotted ALOHA (IRSA) protocol. The scheme, referred to as age-threshold IRSA (AT-IRSA), leverages feedback provided by the receiver to admit to the channel only devices whose AoI exceeds a dynamically adapted target value. By means of detailed networks simulations, as well as of a simple yet tight analytical approximation, we demonstrate that the approach can more than halve the average network AoI compared to plain IRSA, and offers notable improvements over feedback-based state-of-the-art slotted ALOHA solutions recently proposed in the literature

Unified Performance Analysis of Near and Far User in Downlink NOMA System over η - μ Fading Channel

The non-orthogonal multiple access (NOMA) scheme is considered as a frontier technology to cater the requirements of 5G and beyond 5G (B5G) communication systems. To fully exploit the essence of NOMA, it is very important to explore the behavior of NOMA users over the most realistic nonhomogenous fading conditions. In this paper, we derive unified closed-form expressions of the basic performance metrics of the NOMA users. Their performance is evaluated in terms of average bit error rate (ABER), average channel capacity (ACC) and outage probability (OP) over η−μ fading channel. These expressions are in terms of popular functions, such as Meijer G-function and Gauss hypergeometric function, leading to their versatile use in analytical research. Unlike the existing outage probability expressions in terms of Yacoub integral, the derived expressions are easier to implement in software packages like MATLAB. Moreover, we compare the obtained results with a reference system, consisting of genie-aided NOMA system. We interpret that genie-aided performance results provide benchmark bounds for the metrics. Extensive simulations are carried out to validate the derived analytical expressions

Multi-Power Irregular Repetition Slotted ALOHA in Heterogeneous IoT networks

International audienceIrregular Repetition Slotted Aloha (IRSA) is one candidate member of a family of random access protocols to provide solutions for massive parallel connections in the Internet of Things (IoT) networks. The key features of this protocol are repeating the transmitted packets several times and using Successive Interference Cancellation (SIC) at the decoder to resolve the collisions, which dramatically increases the performance of Slotted ALOHA. Motivated by multiple previous studies of IRSA performance in different settings, we focus on the scenario of an IoT network where the packets of different nodes are received with different powers at the base station, either per design due to different transmission power, or induced by the fact that the nodes are at different distances from the base station. In such a scenario, the capture effect emerges at the receiver, which in turn enhances the protocol performance. We analyze the protocol behavior using a new density evolution which is based on dividing nodes into classes with different powers. By computing the probability to decode a packet in the presence of the interference, we explore the achievable throughput and its associated gain and show the excellent performance of Multi-Power IRSA

Q-learning Channel Access Methods for Wireless Powered Internet of Things Networks

The Internet of Things (IoT) is becoming critical in our daily life. A key technology of interest in this thesis is Radio Frequency (RF) charging. The ability to charge devices wirelessly creates so called RF-energy harvesting IoT networks. In particular, there is a hybrid access point (HAP) that provides energy in an on-demand manner to RF-energy harvesting devices. These devices then collect data and transmit it to the HAP. In this respect, a key issue is ensuring devices have a high number of successful transmissions. There are a number of issues to consider when scheduling the transmissions of devices in the said network. First, the channel gain to/from devices varies over time. This means the efficiency to deliver energy to devices and to transmit the same amount of data is different over time. Second, during channel access, devices are not aware of the energy level of other devices nor whether they will transmit data. Third, devices have non-causal knowledge of their energy arrivals and channel gain information. Consequently, they do not know whether they should delay their transmissions in hope of better channel conditions or less contention in future time slots or doing so would result in energy overflow

Performance evaluation of framed slotted ALOHA with reservation packets and succesive interference cancelation for M2M networks

[EN] Random access protocols like ALOHA have been considered for machine-to-machine (M2M) communication in future networks for their simplicity of operation. This paper evaluates the performance of a Frame Slotted-ALOHA protocol that uses reservation and data packets (FSA-RDP), in a scenario where a controller collects data packets transmitted by a finite number of M2M devices. In FSA-RDP, frames of variable duration are divided in two parts, the reservation and data subframes. During the reservation subframe, active devices send short reservation packets to the controller. The controller assigns reserved slots in the data subframe to those devices that succeeded with the reservation. At devices, the FIFO service discipline and two queue management schemes, tail drop and push-out, have been considered. When the queue size is of one packet, we develop a discrete-time Markov chain to evaluate the protocol performance, including the cumulative distribution function of the delay of data packets that are successfully transmitted. Analytical results are validated by extensive simulations. The simulation model is also used to evaluate the system performance when larger queues are used. In addition, we study the impact that implementing Successive Interference Cancellation (SIC) at the controller has on the system performance. We also evaluate the performance of implementing SIC at the controller together with Irregular Repetition Slotted ALOHA (IRSA) to send the reservation packets. Numerical results show that the protocol efficiency of FSA-RDP is between one and two orders of magnitude larger than the efficiency of conventional Frame Slotted ALOHA, when a perfect channel is assumed. In more realistic channel environments, the use of SIC brings an important performance boost.This work has been supported by the Ministry of Economy and Competitiveness of Spain through projects TIN2013-47272-C2-1-R and TEC2015-71932-REDT. The authors would like to thank the support received from the Institute ITACA (Instituto Universitario de Tecnologias de la Informacion y Comunicaciones) at the Universitat Politecnica de Valencia, Spain. C. Portillo acknowledges the funding received from the European Union under the program Erasmus Mundus Partnerships, project EuroinkaNet, GRANT AGREEMENT NUMBER -2014 -0870/001/001, and the support received from SEP-SES (DSA/103.5/15/6629).Casares-Giner, V.; Martínez Bauset, J.; Portillo, C. (2019). Performance evaluation of framed slotted ALOHA with reservation packets and succesive interference cancelation for M2M networks. Computer Networks. 155:15-30. https://doi.org/10.1016/j.comnet.2019.02.021S153015

A Regret Minimization Approach to Frameless Irregular Repetition Slotted Aloha: IRSA-RM

International audienceWireless communications play an important part in the systems of the Internet of Things (IoT). Recently, there has been a trend towards long-range communications systems for the IoT, including cellular networks. For many use cases, such as massive machine-type communications (mMTC), performance can be gained by moving away from the classical model of connection establishment and adopting random access methods. Associated with physical layer techniques such as Successive Interference Cancellation (SIC), or Non-Orthogonal Multiple Access (NOMA), the performance of random access can be dramatically improved, giving rise to novel random access protocol designs. This article studies one of these modern random access protocols: Irregular Repetition Slotted Aloha (IRSA). Since optimizing its parameters is not an easily solved problem, in this article we use a reinforcement learning approach for that purpose. We adopt one specific variant of reinforcement learning, Regret Minimization, to learn the protocol parameters. We explain why it is selected, how to apply it to our problem with centralized learning, and finally, we provide both simulation results and insights into the learning process. The results obtained show the excellent performance of IRSA when it is optimized with Regret Minimization