Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.Comment: 15 pages, 15 figure

Protocol for Extreme Low Latency M2M Communication Networks

As technology evolves, more Machine to Machine (M2M) deployments and mission critical services are expected to grow massively, generating new and diverse forms of data traffic, posing unprecedented challenges in requirements such as delay, reliability, energy consumption and scalability. This new paradigm vindicates a new set of stringent requirements that the current mobile networks do not support. A new generation of mobile networks is needed to attend to this innovative services and requirements - the The fifth generation of mobile networks (5G) networks. Specifically, achieving ultra-reliable low latency communication for machine to machine networks represents a major challenge, that requires a new approach to the design of the Physical (PHY) and Medium Access Control (MAC) layer to provide these novel services and handle the new heterogeneous environment in 5G. The current LTE Advanced (LTE-A) radio access network orthogonality and synchronization requirements are obstacles for this new 5G architecture, since devices in M2M generate bursty and sporadic traffic, and therefore should not be obliged to follow the synchronization of the LTE-A PHY layer. A non-orthogonal access scheme is required, that enables asynchronous access and that does not degrade the spectrum. This dissertation addresses the requirements of URLLC M2M traffic at the MAC layer. It proposes an extension of the M2M H-NDMA protocol for a multi base station scenario and a power control scheme to adapt the protocol to the requirements of URLLC. The system and power control schemes performance and the introduction of more base stations are analyzed in a system level simulator developed in MATLAB, which implements the MAC protocol and applies the power control algorithm. Results showed that with the increase in the number of base stations, delay can be significantly reduced and the protocol supports more devices without compromising delay or reliability bounds for Ultra-Reliable and Low Latency Communication (URLLC), while also increasing the throughput. The extension of the protocol will enable the study of different power control algorithms for more complex scenarios and access schemes that combine asynchronous and synchronous access

Signal Processing and Learning for Next Generation Multiple Access in 6G

Wireless communication systems to date primarily rely on the orthogonality of resources to facilitate the design and implementation, from user access to data transmission. Emerging applications and scenarios in the sixth generation (6G) wireless systems will require massive connectivity and transmission of a deluge of data, which calls for more flexibility in the design concept that goes beyond orthogonality. Furthermore, recent advances in signal processing and learning have attracted considerable attention, as they provide promising approaches to various complex and previously intractable problems of signal processing in many fields. This article provides an overview of research efforts to date in the field of signal processing and learning for next-generation multiple access, with an emphasis on massive random access and non-orthogonal multiple access. The promising interplay with new technologies and the challenges in learning-based NGMA are discussed

Towards Massive Connectivity Support for Scalable mMTC Communications in 5G networks

The fifth generation of cellular communication systems is foreseen to enable a multitude of new applications and use cases with very different requirements. A new 5G multiservice air interface needs to enhance broadband performance as well as provide new levels of reliability, latency and supported number of users. In this paper we focus on the massive Machine Type Communications (mMTC) service within a multi-service air interface. Specifically, we present an overview of different physical and medium access techniques to address the problem of a massive number of access attempts in mMTC and discuss the protocol performance of these solutions in a common evaluation framework