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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In this paper, we study the problem of decentralized scheduling in Interference Channels (IC). In this setting, each Transmitter (TX) receives an arbitrary amount of feedback regarding the global multi-user channel state based on which it decides whether to transmit or to stay silent without any form of communication with the other TXs. While many methods have been proposed to tackle the problem of link scheduling in the presence of reliable Channel State Information (CSI), finding the optimally robust transmission strategy in the presence of arbitrary channel uncertainties at each TX has remained elusive for the past years. In this work, we recast the link scheduling problem as a decentralized classification problem and we propose the use of Collaborative Deep Neural Networks (C-DNNs) to solve this problem. After adequate training, the scheduling obtained using the C-DNNs flexibly adapts to the decentralized CSI configuration to outperform other scheduling algorithms.

Decentralized Deep Scheduling for Interference Channels.

The two-user Multiple-Input Multiple-Output (MIMO) broadcast channel (BC) with arbitrary antenna configuration is considered, in which the transmitter obtains (i) delayed channel state information (CSI) from a latency-prone feedback channel as well as (ii) imperfect current CSI, e.g., from prediction based on these past channel samples. The degrees of freedom (DoF) region under such a setting is fully characterized as a function of a prediction quality exponent. This work extends prior work, previously limited to MISO, to fully general antenna settings. An intriguing by-product of our results is to reveal the benefits of dealing with an asymmetric multi-user MIMO configuration (i.e., one in which terminals do not have the same number of antennas) in the case of non-perfect CSIT (e.g., caused by feedback delays or limited preciseness).

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Degrees of freedom of time-correlated broadcast channels with delayed CSIT: The MIMO case

The problem of optimal Power Policy (PP) design for hybrid interweave/underlay Cognitive Radio (CR) systems is investigated, in the presence of imperfect Spectrum Sensing (SS). A limited Channel State Information at the Transmitter (CSIT) scenario is considered, that has never been studied before, where the Secondary Transmitter (STx) uses combined instantaneous and statistical CSIT. Given such CSIT, the optimal PP with the aim of maximizing the average transmission rate of the CR system, subject to a constraint on the average interference caused by STx transmission to the primary system, is found. The derived PP is then exploited for the development of an iterative framework for the problem of jointly optimizing the sensing time and the applied PP. By means of simulations, it is shown that the proposed joint SS and PP optimization framework offers a clear gain in terms of the achievable rate of the CR system, with respect to conventional underlay and interweave CR systems, especially for intermediate values of the average interference constraint and also for different primary user activity regimes.

Joint Power and Sensing Optimization for Hybrid Cognitive Radios with limited CSIT

Method of multicell cooperative radio communications in which: the terminals estimate and communicate to all the access points of a cooperating group of access points information about the transmission channels between them and each access point of the group, the access points have homologous means of processing for ordering the servicing of the terminals and operate according to a determined criterion common to all the access points, the servicing of the terminals is performed jointly by the access points, in an order determined by said processing means, using transmission parameters on the basis of the information about the transmission channels which is transmitted by the terminals.

Multicell cooperative communications in a decentralized network

We consider a MIMO broadcast channel where the channel state information at the transmitter (CSIT), to be used for user scheduling and beamforming, is gained through a limited-rate feedback channel. In view of optimizing the overall spectral efficiency of the system, we propose an adaptive scheme in which the feedback rate is no longer constant but rather optimized as a function of the time-dependent channel quality seen at the user side. One key idea is that, under an average feedback rate constraint, a user ought to provide more feedback at moments when it is more likely to be scheduled.We provide the theoretical grounds for our approach then derive quasi-optimal feedback resource allocation schemes, the performance of which is illustrated through Monte Carlo simulations.

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David Gesbert

Papers

Decentralized Deep Scheduling for Interference Channels.

Degrees of freedom of time-correlated broadcast channels with delayed CSIT: The MIMO case

Joint Power and Sensing Optimization for Hybrid Cognitive Radios with limited CSIT

Multicell cooperative communications in a decentralized network

Adaptive feedback rate control in MIMO broadcast systems