Wireless Communications

What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.

What Will 5G Be

A cellular base station serves a multiplicity of single-antenna terminals over the same time-frequency interval. Time-division duplex operation combined with reverse-link pilots enables the base station to estimate the reciprocal forward- and reverse-link channels. The conjugate-transpose of the channel estimates are used as a linear precoder and combiner respectively on the forward and reverse links. Propagation, unknown to both terminals and base station, comprises fast fading, log-normal shadow fading, and geometric attenuation. In the limit of an infinite number of antennas a complete multi-cellular analysis, which accounts for inter-cellular interference and the overhead and errors associated with channel-state information, yields a number of mathematically exact conclusions and points to a desirable direction towards which cellular wireless could evolve. In particular the effects of uncorrelated noise and fast fading vanish, throughput and the number of terminals are independent of the size of the cells, spectral efficiency is independent of bandwidth, and the required transmitted energy per bit vanishes. The only remaining impairment is inter-cellular interference caused by re-use of the pilot sequences in other cells (pilot contamination) which does not vanish with unlimited number of antennas.

Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas

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

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We address the problem of resource allocation (RA) in a cognitive radio (CR) communication system with multiple secondary operators sharing spectrum with an incumbent primary operator. The key challenge of the RA problem is the inter-operator coordination arising in the optimization problem so that the aggregated interference at the primary users (PUs) does not exceed the target threshold. While this problem is easily solvable if a centralized unit could access information of all secondary operators, it becomes challenging in a realistic scenario. In this paper, considering a satellite setting, we alleviate this problem by proposing two approaches to reduce the information exchange level among the secondary operators. In the first approach, we formulate an RA scheme based on a partial information sharing method which enables distributed optimization across secondary operators. In the second approach, instead of exchanging secondary users (SUs) information, the operators only exchange their contributions of the interference-level and RA is performed locally across secondary operators. These two approaches, for the first time in this context, provide a trade-off between performance and level of inter-operator information exchange. Through the numerical simulations, we explain this trade-off and illustrate the penalty resulting from partial information exchange.

Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems

This paper addresses vehicle positioning, a topic whose importance has risen dramatically in the context of future autonomous driving systems. While classical methods that use GPS and/or beacon signals from network infrastructure for triangulation tend to be sensitive to multi-paths and signal obstruction, our method exhibits robustness with respect to such phenomena. Our approach builds on the recently proposed Channel-SLAM method which first enabled leveraging of multi-path so as to improve (single) vehicle positioning. Here, we propose a cooperative mapping approach which builds upon the Channel-SLAM concept, referred to here as Team Channel-SLAM. Team Channel-SLAM not only exploits the stationary nature of many reflecting objects around the vehicle, but also capitalizes on the multi-vehicle nature of road traffic. The key intuition behind our method is the exploitation for the first time of the correlation between reflectors around multiple neighboring vehicles. An algorithm is derived for reflector selection and estimation, combined with a team particle filter (TPF) so as to achieve high precision simultaneous multiple vehicle positioning. We obtain large improvement over the single-vehicle positioning scenario, with gains being already noticeable for moderate vehicle densities, such as over 40% improvement for a vehicle density as low as 4 vehicles in 132 meters' length road.

Vehicle Localization via Cooperative Channel Mapping.

Decentralized decisional networks are composed of agents that take coordinated decisions on the basis of individual noisy information about the system state, i.e. under a so called distributed state information configuration. In such scenarios, the application of algorithms directly derived from classical centralized optimization often incurs severe performance degradation. This is because the agents fail to predict each other’s decision due to local state information noise, hence they do not coordinate properly. On the other hand coordination can be easily enhanced by letting one agent reduce its dependency with respect to its state information, thus making its decision more predictable but less adapted to the system state. This observation naturally leads to formulating a fundamental trade-off, coined here predictability-distortion trade off. The goal of this paper is to formulate this trade-off and propose a framework to explore it, based on a concept of quantization under predictability constraint.

On A Fundamental Predictability-Distortion Trade-Off in Decentralized Decisional Networks

For setting cooperation parameters of a communication system including a plurality of node devices interconnected by links and adapted to be respectively configured according to said cooperation parameters, said cooperation parameters having an impact on an environment of the communication system being represented by measurable random variables, a figure of merit relative to performance of the communication system regarding said environment having to be optimized for determining said cooperation parameters, a cooperation phase comprises: gathering measurements data representative of measurements of the random variables; optimizing the figure of merit for determining said cooperation parameters, on the basis of the obtained measurements. At least one link implying quantization operations relying on a codebook for gathering said measurements data and/or for providing said cooperation parameters, a pre-processing phase comprises beforehand: obtaining statistics data relative to a probability distribution of said random variables; and determining every codebook on the basis of the figure of merit, such that the figure of merit is statistically optimized according to the obtained statistics.

Method for determining quantization codebooks in a cooperative communication system

For determining, in a distributed fashion, precoders to be applied for performing transmissions of data between a plurality of transmitters and a plurality of receivers via a global MIMO channel H of a wireless communication system, said precoders jointly forming an overall precoder V, each and every j-th transmitter perform: gathering long-term statistics of CSIT errors incurred by each one of the transmitters; obtaining short-term CSIT related data and building therefrom its own view Ĥ(j) of the global MIMO channel H; determining an estimate {tilde over (V)}(j) of the overall precoder V from the short-term CSIT related data; refining the estimate {tilde over (V)}(j) on the basis of the gathered long-term statistics of CSIT errors so as to obtain a refined precoder {tilde over (V)}(j) that is a view of the overall precoder V from the standpoint of said j-th transmitter, further on the basis of its own view Ĥ(j) of the global MIMO channel H, and further on the basis of a figure of merit representative of performance of said transmissions via the global MIMO channel H.

David Gesbert

Papers

Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems

Vehicle Localization via Cooperative Channel Mapping.

On A Fundamental Predictability-Distortion Trade-Off in Decentralized Decisional Networks

Method for determining quantization codebooks in a cooperative communication system

Method and device for performing transmissions of data