Sparse Signal Processing Concepts for Efficient 5G System Design

As it becomes increasingly apparent that 4G will not be able to meet the emerging demands of future mobile communication systems, the question what could make up a 5G system, what are the crucial challenges and what are the key drivers is part of intensive, ongoing discussions. Partly due to the advent of compressive sensing, methods that can optimally exploit sparsity in signals have received tremendous attention in recent years. In this paper we will describe a variety of scenarios in which signal sparsity arises naturally in 5G wireless systems. Signal sparsity and the associated rich collection of tools and algorithms will thus be a viable source for innovation in 5G wireless system design. We will discribe applications of this sparse signal processing paradigm in MIMO random access, cloud radio access networks, compressive channel-source network coding, and embedded security. We will also emphasize important open problem that may arise in 5G system design, for which sparsity will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces

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

A NOMA-enhanced reconfigurable access scheme with device pairing for M2M networks

This paper aims to address the distinct requirements of machine-to-machine networks, particularly heterogeneity and massive transmissions. To this end, a reconfigurable medium access control (MAC) with the ability to choose a proper access scheme with the optimal configuration for devices based on the network status is proposed. In this scheme, in each frame, a separate time duration is allocated for each of the nonorthogonal multiple access (NOMA)-based, orthogonal multiple access (OMA)-based, and random access-based segments, where the length of each segment can be optimized. To solve this optimization problem, an iterative algorithm consisting of two sub-problems is proposed. The first sub-problem deals with selecting devices for the NOMA/OMA-based transmissions, while the second one optimizes the parameter of the random access scheme. To show the efficacy of the proposed scheme, the results are compared with the reconfigurable scheme which does not support NOMA. The results demonstrate that by using a proper device pairing scheme for the NOMA-based transmissions, the proposed reconfigurable scheme achieves better performance when NOMA is adopted

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