Machine-type communications (MTC) are expected to play an essential role within future 5G systems. In the FP7 project METIS, MTC has been further classified into "massive Machine-Type Communication" (mMTC) and "ultra-reliable Machine-Type Communication" (uMTC). While mMTC is about wireless connectivity to tens of billions of machine-type terminals, uMTC is about availability, low latency, and high reliability. The main challenge in mMTC is scalable and efficient connectivity for a massive number of devices sending very short packets, which is not done adequately in cellular systems designed for human-type communications. Furthermore, mMTC solutions need to enable wide area coverage and deep indoor penetration while having low cost and being energy efficient. In this article, we introduce the physical (PHY) and medium access control (MAC) layer solutions developed within METIS to address this challenge.

5G will have to support a multitude of new applications with a wide variety of requirements, including higher peak and user data rates, reduced latency, enhanced indoor coverage, increased number of devices, and so on. The expected traffic growth in 10 or more years from now can be satisfied by the combined use of more spectrum, higher spectral efficiency, and densification of cells. The focus of the present article is on advanced techniques for higher spectral efficiency and improved coverage for cell edge users. We propose a smart combination of small cells, joint transmission coordinated multipoint (JT CoMP), and massive MIMO to enhance the spectral efficiency with affordable complexity. We review recent achievements in the transition from theoretical to practical concepts and note future research directions. We show in measurements with macro-plus-small-cell scenarios that spectral efficiency can be improved by flexible clustering and efficient user selection, and that adaptive feedback compression is beneficial to reduce the overhead significantly. Moreover, we show in measurements that fast feedback reporting combined with advanced channel prediction are able to mitigate the impairment effects of JT CoMP

5G networks will be the first generation to benefit from location information that is sufficiently precise to be leveraged in wireless network design and optimization. We argue that location information can aid in addressing several of the key challenges in 5G, complementary to existing and planned technological developments. These challenges include an increase in traffic and number of devices, robustness for mission-critical services, and a reduction in total energy consumption and latency. This paper gives a broad overview of the growing research area of location-aware communications across different layers of the protocol stack. We highlight several promising trends, tradeoffs, and pitfalls. I. 5G: INTRODUCTION AND CHALLENGES 5 G will be characterized by a wide variety of use cases, as well as orders-of-magnitude increases in mobile data volume per area, number of connected devices, and typical user data rate, all compared to current mobile communication systems [1]. To cope with these demands, a number of challenges must be addressed before 5G can be successfully deployed. These include the demand for extremely high data rates and much lower latencies, potentially down to 1 ms end-to-end for certain applications [2]. Moreover, scalability and reduction of signaling overhead must be accounted for, as well as minimization of (total) energy consumption to enable affordable cost for network operation. To fulfill these requirements in 5G, network densification is key, calling for a variety of coordination and cooperation techniques between various kinds of network elements in an ultra-dense heterogeneous network. Moreover, by implementing sharing and coexistence approaches, along with new multi-GHz frequency bands, spectrum efficiency can be improved. An overview of a number of disruptive technologies for 5G is provided in [1]. It is our vision that context information in general, and location information in particular, can complement both traditional and disruptive technologies in addressing several of the challenges in 5G networks. While location information was available in previous generations of cellular mobile radio systems, e.g., cell-ID positioning in 2G, timing-based positioning using communication-relevant synchronization signals in 3G, and additionally dedicated positioning reference signals in 4G, accuracy ranged from hundreds to tens of meters, rendering position information insufficiently precise for some communications operations. In 5G, for the first time, a majority of

Herein, we focus on convergent 6G communication, localization and sensing systems by identifying key technology enablers, discussing their underlying challenges, implementation issues, and recommending potential solutions. Moreover, we discuss exciting new opportunities for integrated localization and sensing applications, which will disrupt traditional design principles and revolutionize the way we live, interact with our environment, and do business. Regarding potential enabling technologies, 6G will continue to develop towards even higher frequency ranges, wider bandwidths, and massive antenna arrays. In turn, this will enable sensing solutions with very fine range, Doppler, and angular resolutions, as well as localization to cm-level degree of accuracy. Besides, new materials, device types, and reconfigurable surfaces will allow network operators to reshape and control the electromagnetic response of the environment. At the same time, machine learning and artificial intelligence will leverage the unprecedented availability of data and computing resources to tackle the biggest and hardest problems in wireless communication systems. As a result, 6G will be truly intelligent wireless systems that will provide not only ubiquitous communication but also empower high accuracy localization and high-resolution sensing services. They will become the catalyst for this revolution by bringing about a unique new set of features and service capabilities, where localization and sensing will coexist with communication, continuously sharing the available resources in time, frequency, and space. This work concludes by highlighting foundational research challenges, as well as implications and opportunities related to privacy, security, and trust.The associate editor coordinating the review of this manuscript and approving it for publication was Ahmed Farouk .

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