The evolution toward 5G mobile networks will be characterized by an increasing number of wireless devices, increasing device and service complexity, and the requirement to access mobile services ubiquitously. Two key enablers will allow the realization of the vision of 5G: very dense deployments and centralized processing. This article discusses the challenges and requirements in the design of 5G mobile networks based on these two key enablers. It discusses how cloud technologies and flexible functionality assignment in radio access networks enable network densification and centralized operation of the radio access network over heterogeneous backhaul networks. The article describes the fundamental concepts, shows how to evolve the 3GPP LTE a

In rich-scattering environments, layered space-time architectures like the BLAST system may exploit the capacity advantage of multiple antenna systems. We present a novel, computationally efficient algorithm for detecting V-BLAST architectures with respect to the MMSE criterion. It utilizes a sorted QR decomposition of the channel matrix and leads to a simple successive detection structure. The new algorithm needs only a fraction of computational effort compared to the standard V-BLAST algorithm and achieves the same error performance.

The next frontier towards truly ubiquitous connectivity is the use of Low Earth Orbit (LEO) small-satellite constellations to support 5G and Beyond-5G (B5G) networks. Besides enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC), LEO constellations can support ultra-reliable communications (URC) with relaxed latency requirements of a few tens of milliseconds. Small-satellite impairments and the use of low orbits pose major challenges to the design and performance of these networks, but also open new innovation opportunities. This paper provides a comprehensive overview of the physical and logical links, along with the essential architectural and technological components that enable the full integration of LEO constellations into 5G and B5G systems. Furthermore, we characterize and compare each physical link category and explore novel techniques to maximize the achievable data rates.

Cloud computing draws significant attention in the information technology (IT) community as it provides ubiquitous on-demand access to a shared pool of configurable computing resources with minimum management effort. It gains also more impact on the communication technology (CT) community and is currently discussed as an enabler for flexible, cost-efficient and more powerful mobile network implementations. Although centralized baseband pools are already investigated for the radio access network (RAN) to allow for efficient resource usage and advanced multicell algorithms, these technologies still require dedicated hardware and do not offer the same characteristics as cloud-computing platforms, i.e., on-demand provisioning, virtualization, resource pooling, elasticity, service metering, and multitenancy. However, these properties of cloud computing are key enablers for future mobile communication systems characterized by an ultradense deployment of radio access points (RAPs) leading to severe multicell interference in combination with a significant increase of the number of access nodes and huge fluctuations of the rate requirements over time. In this article, we will explore the benefits that cloud computing offers for fifth-generation (5G) mobile networks and investigate the implications on the signal processing algorithms.

In recent publications the use of lattice-reduction for signal detection in multiple antenna systems has been proposed. In this paper, we adopt these lattice-reduction-aided schemes to the MMSE criterion. We show that an obvious way to do this is infeasible and propose an alternative method based on an extended system model, which in conjunction with simple successive interference cancellation nearly reaches the performance of maximum-likelihood detection. Furthermore, we demonstrate that, a sorted QR decomposition can significantly reduce the computational effort associated with lattice-reduction. Thus, the new algorithm clearly outperforms existing methods with comparable complexity.

Lattice reduction is a powerful concept for solving diverse problems involving point lattices. Signal processing applications where lattice reduction has been successfully used include global positioning system (GPS), frequency estimation, color space estimation in JPEG pictures, and particularly data detection and precoding in wireless communication systems. In this article, we first provide some background on point lattices and then give a tutorial-style introduction to the theoretical and practical aspects of lattice reduction. We describe the most important lattice reduction algorithms and comment on their performance and computational complexity. Finally, we discuss the application of lattice reduction in wireless communications and statistical signal processing. Throughout the article, we point out open problems and interesting questions for future research.