Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation. Extra antennas help by focusing energy into ever smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include extensive use of inexpensive low-power components, reduced latency, simplification of the MAC layer, and robustness against intentional jamming. The anticipated throughput depends on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly joined terminals, the exploitation of extra degrees of freedom provided by the excess of service antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This article presents an overview of the massive MIMO concept and contemporary research on the topic.

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Massive MIMO for next generation wireless systems

Traditionally, the frequency spectrum is licensed to users by government agencies in a rigid manner where the licensee has the exclusive right to access the allocated band. Therefore, licensees are protected from any interference all the time. From a practical standpoint, however, an unlicensed (secondary) user may share a frequency band with its licensed (primary) owner as long as the interference it incurs is not deemed harmful by the licensee. In a fading environment, a secondary user may take advantage of this fact by opportunistically transmitting with high power when its signal, as received by the licensed receiver, is deeply faded. In this paper we investigate the capacity gains offered by this dynamic spectrum sharing approach when channels vary due to fading. In particular, we quantify the relation between the secondary channel capacity and the interference inflicted on the primary user. We further evaluate and compare the capacity under different fading distributions. Interestingly, our results indicate a significant gain in spectrum access in fading environments compared to the deterministic case

Fundamental limits of spectrum-sharing in fading environments

Integrating the various embedded devices and systems in our environment enables an Internet of Things (IoT) for a smart city. The IoT will generate tremendous amount of data that can be leveraged for safety, efficiency, and infotainment applications and services for city residents. The management of this voluminous data through its lifecycle is fundamental to the realization of smart cities. Therefore, in contrast to existing surveys on smart cities we provide a data-centric perspective, describing the fundamental data management techniques employed to ensure consistency, interoperability, granularity, and reusability of the data generated by the underlying IoT for smart cities. Essentially, the data lifecycle in a smart city is dependent on tightly coupled data management with cross-cutting layers of data security and privacy, and supporting infrastructure. Therefore, we further identify techniques employed for data security and privacy, and discuss the networking and computing technologies that enable smart cities. We highlight the achievements in realizing various aspects of smart cities, present the lessons learned, and identify limitations and research challenges.

Smart Cities: A Survey on Data Management, Security, and Enabling Technologies

A survey on 5G

Методы подавления шумов и помех в электронных системах. (Noise reduction techniques in electronic systems)

Wireless solutions are rapidly growing in machine-to-machine communications in industrial environments. These environments provide challenging conditions in terms of radio wave propagation as well as electromagnetic interference. In this article, results from the characterization of radio channel properties are summarized in order to provide some guidelines for the choice of wireless solutions in industrial environments. In conclusion, it is essential to know the sensitivity of industrial processes to time delay in data transfer. Furthermore, it is important to be aware of the radio interference environment and the manner in which different wireless technologies react upon interference. These steps will minimize the risk of unforeseen expensive disturbances in industrial processes.

Challenges and conditions for wireless machine-to-machine communications in industrial environments

Wireless Machine-to-Machine Communications in Industrial Environments

Middleton's class A interference model has properties that make it possible to represent a wide class of interference signals. By choice of model parameters, interference signals ranging from pure Gaussian distributed to pure impulsive interference can be modelled. These properties make the model very useful for a large variety of applications. However, an expression for the channel capacity of the class A interference channel has not yet been published. The channel capacity of this model is derived, and numerical examples are given for some useful sets of model parameters.

Channel capacity of Middleton's class A interference channel

A robust and accurate positioning solution is required to increase the safety in GPS-denied environments. Although there is a lot of available research in this area, little has been done for confined environments such as tunnels. Therefore, we organized a measurement campaign in a basement tunnel of Linkoping university, in which we obtained ultra-wideband (UWB) complex impulse responses for line-of-sight (LOS), and three non-LOS (NLOS) scenarios. This paper is focused on time-of-arrival (TOA) ranging since this technique can provide the most accurate range estimates, which are required for range-based positioning. We describe the measurement setup and procedure, select the threshold for TOA estimation, analyze the channel propagation parameters obtained from the power delay profile (PDP), and provide statistical model for ranging. According to our results, the rise-time should be used for NLOS identification, and the maximum excess delay should be used for NLOS error mitigation. However, the NLOS condition cannot be perfectly determined, so the distance likelihood has to be represented in a Gaussian mixture form. We also compared these results with measurements from a mine tunnel, and found a similar behavior.

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Measurement Analysis and Channel Modeling for TOA-Based Ranging in Tunnels

Electrical and mechanical machinery, highly reflective industrial facilities and co-existing communication systems are the major sources of disturbances in wireless industrial applications. Characterization of industrial environments is important for the development of standards, to assess current and future deployment of wireless technologies, and to provide systems integrators and end user with guidelines. In this paper some deductions from measurements carried out at three industrial environments using traditional electric field strength, amplitude probability distribution (APD) and multipath time dispersion measurements are presented. These measurements have given surprising and interesting results.

Peter Stenumgaard

Papers

Challenges and conditions for wireless machine-to-machine communications in industrial environments

Wireless Machine-to-Machine Communications in Industrial Environments

Channel capacity of Middleton's class A interference channel

Measurement Analysis and Channel Modeling for TOA-Based Ranging in Tunnels

Sources of disturbances on wireless communication in industrial and factory environments