Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to the rates of multiple gigabit per second per user Though mmWave can be readily used in stationary scenarios, such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond) We first summarize the recent channel measurement campaigns and modeling results Then, we discuss in detail recent progresses in multiple input multiple output transceiver design for mmWave communications After that, we provide an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed

Millimeter Wave Communications for Future Mobile Networks

Cognitive radios have been proposed as a means to implement efficient reuse of the licensed spectrum. The key feature of a cognitive radio is its ability to recognize the primary (licensed) user and adapt its communication strategy to minimize the interference that it generates. We consider a communication scenario in which the primary and the cognitive user wish to communicate to different receivers, subject to mutual interference. Modeling the cognitive radio as a transmitter with side-information about the primary transmission, we characterize the largest rate at which the cognitive radio can reliably communicate under the constraint that (i) no interference is created for the primary user, and (ii) the primary encoder-decoder pair is oblivious to the presence of the cognitive radio.

Cognitive Radio: An Information-Theoretic Perspective

Supporting high mobility in millimeter wave (mmWave) systems enables a wide range of important applications, such as vehicular communications and wireless virtual/augmented reality. Realizing this in practice, though, requires overcoming several challenges. First, the use of narrow beams and the sensitivity of mmWave signals to blockage greatly impact the coverage and reliability of highly-mobile links. Second, highly-mobile users in dense mmWave deployments need to frequently hand-off between base stations (BSs), which is associated with critical control and latency overhead. Furthermore, identifying the optimal beamforming vectors in large antenna array mmWave systems requires considerable training overhead, which significantly affects the efficiency of these mobile systems. In this paper, a novel integrated machine learning and coordinated beamforming solution is developed to overcome these challenges and enable highly-mobile mmWave applications. In the proposed solution, a number of distributed yet coordinating BSs simultaneously serve a mobile user. This user ideally needs to transmit only one uplink training pilot sequence that will be jointly received at the coordinating BSs using omni or quasi-omni beam patterns. These received signals draw a defining signature not only for the user location, but also for its interaction with the surrounding environment. The developed solution then leverages a deep learning model that learns how to use these signatures to predict the beamforming vectors at the BSs. This renders a comprehensive solution that supports highly mobile mmWave applications with reliable coverage, low latency, and negligible training overhead. Extensive simulation results based on accurate ray-tracing, show that the proposed deep-learning coordinated beamforming strategy approaches the achievable rate of the genie-aided solution that knows the optimal beamforming vectors with no training overhead. Compared with traditional beamforming solutions, the results show that the proposed deep learning-based strategy attains higher rates, especially in high-mobility large-array regimes.

Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems

Millimeter wave (mm-wave) communications is considered a promising technology for 5G networks. Exploiting beamforming gains with large-scale antenna arrays to combat the increased path loss at mm-wave bands is one of the defining features. However, previous works on mm-wave network analysis usually adopted oversimplified antenna patterns for tractability, which can lead to significant deviation from the performance with actual antenna patterns. In this paper, using tools from stochastic geometry, we carry out a comprehensive investigation on the impact of directional antenna arrays in mm-wave networks. We first present a general and tractable framework for coverage analysis with arbitrary distributions for interference power and arbitrary antenna patterns. It is then applied to mm-wave ad hoc and cellular networks, where two sophisticated antenna patterns with desirable accuracy and analytical tractability are proposed to approximate the actual antenna pattern. Compared with previous works, the proposed approximate antenna patterns help to obtain more insights on the role of directional antenna arrays in mm-wave networks. In particular, it is shown that the coverage probabilities of both types of networks increase as a non-decreasing concave function with the antenna array size. The analytical results are verified to be effective and reliable through simulations, and numerical results also show that large-scale antenna arrays are required for satisfactory coverage in mm-wave networks.

Coverage Analysis for Millimeter Wave Networks: The Impact of Directional Antenna Arrays

Connected and autonomous vehicles will play a pivotal role in future intelligent transportation systems and smart cities, in general. High-speed and low-latency wireless communication links will allow municipalities to warn vehicles against safety hazards, as well as support cloud-driving solutions to drastically reduce traffic jams and air pollution. To achieve these goals, vehicles need to be equipped with a wide range of sensors generating and exchanging high rate data streams. Recently, millimeter wave (mmWave) techniques have been introduced as a means of fulfilling such high data rate requirements. In this paper, we model a highway communication network and characterize its fundamental link budget metrics. In particular, we specifically consider a network where vehicles are served by mmWave base stations (BSs) deployed alongside the road. To evaluate our highway network, we develop a new theoretical model that accounts for a typical scenario where heavy vehicles (such as buses and lorries) in slow lanes obstruct line-of-sight (LOS) paths of vehicles in fast lanes and, hence, act as blockages. Using tools from stochastic geometry, we derive approximations for the signal-to-interference-plus-noise Ratio (SINR) outage probability, as well as the probability that a user achieves a target communication rate (rate coverage probability). Our analysis provides new design insights for mmWave highway communication networks. In considered highway scenarios, we show that reducing the horizontal beamwidth from $90^\circ$ to $30^\circ$ determines a minimal reduction in the SINR outage probability (namely $4 \cdot 10^{-2}$ at maximum). Also, unlike bidimensional mmWave cellular networks, for small BS densities (namely one BS every $500\,\text{m}$ ) it is still possible to achieve an SINR outage probability smaller than 0.2.

https://hal.archives-ouvertes.fr/hal-01671182/document

Modeling and Design of Millimeter-Wave Networks for Highway Vehicular Communication

Signal outage, due to shadowing and blockage, is expected to be the main bottleneck in millimeter wave (mmWave) networks. Moreover, the anticipated dense deployment of base stations in mmWave networks is expected to increase the interference from strong line-of-sight base stations too, thus further increasing the probability of outage. To address the issue of reducing outage, this paper explores the possibility of base station co-operation in the downlink of mmWave heterogenous networks. The main focus of this work is showing that, in a stochastic geometry framework that incorporates blockage, co-operation from randomly located base stations decreases the probability of outage/increases the coverage probability. Coverage probabilities are derived accounting for: blockage, different fading distributions on the direct links (but always Rayleigh fading on the interference links), antenna directionality, and different tiers. Numerical results suggest that coverage with base station co-operation in dense mmWave systems (i.e., with high average number of base stations per square meter), without small scale fading on the direct communications links, and with any probability of signal blockage, considerably exceeds coverage without co-operation. In contrast, a small increase in coverage is reported when mmWave networks are less dense, have a high probability of signal blockage and the direct communications links are affected by Rayleigh fading.

Coverage in mmWave Cellular Networks With Base Station Co-Operation

The presence of signal outage, due to shadowing and blockage, is expected to be the main bottleneck in millimeter wave (mmWave) networks. Moreover, with the anticipated vision that mmWave networks would have a dense deployment of base stations, interference from strong line-of-sight base stations increases too, thus further increasing the probability of outage. To address the issue of reducing outage, this paper explores the possibility of base station cooperation in the downlink of a mmWave heterogenous network. The main focus of this work is showing that, in a stochastic geometry framework, cooperation from randomly located base stations decreases outage probability. With the presumed vision that less severe fading will be experienced due to highly directional transmissions, one might expect that cooperation would increase the coverage probability; our numerical examples suggest that is in fact the case. Coverage probabilities are derived accounting for: different fading distributions, antenna directionality and blockage. Numerical results suggest that coverage with base station cooperation in dense mmWave systems and with no small scale fading considerably exceeds coverage with no cooperation. In contrast, an insignificant increase is reported when mmWave networks are less dense with a high probability of signal blockage and with Rayleigh fading.

Coverage in mmWave Cellular Networks with Base station Cooperation

A method for beamforming training is disclosed including SNR values with each of the SNR values associated with one of the antenna identifiers (IDs) in a first frame identifying different radio frequency (RF) chains for a process of beamforming training; receiving and transmitting the first frame bi-directionally between multiple pairs of communicating nodes, wherein each pair of the communicating nodes has at least one initiator node and one responder node; receiving feedback of the SNRs associated with the antenna IDs from two or more responder nodes of the multiple pairs of the communicating nodes to choose an RF chain as favorable transmit antenna for transmission; and determining respective one from the multiple antenna sectors of the multiple antennas for transmission for the responder nodes according to the feedback. A central network controller and a system are also disclosed herein.

Method and Apparatus for Multiuser MIMO Beamforming Training

To be considered for an IEEE Jack Keil Wolf ISIT Student Paper Award. This paper characterizes the ergodic capacity of the fading multiple-input single-output (MISO) channel with per-antenna power constraints (PerPC) with perfect Channel State Information (CSI) at all terminals. This turns out to be the sum-capacity achieving strategy for the ergodic fading Gaussian overlay cognitive interference channel (EGCIFC) in the strong interference regime. The EGCIFC is a two-user timevarying interference channel in which a primary / licensed transmitter and a secondary / cognitive transmitter share the same spectrum and where the cognitive transmitter has noncausal knowledge of the primary user’s message. The MISO and the EGCIFC results are verified numerically for the case of independent Rayleigh fading gains. Different achievable strategies, corresponding to different amount of CSI, are compared to show the performance of the derived PerPC optimal power allocation.

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The capacity of the ergodic miso channel with per-antenna power constraint and an application to the fading cognitive interference channel

This paper provides a survey of the state-of-the-art information theoretic analysis for overlay multi-user (more than two pairs) cognitive networks and reports new capacity results. In an overlay scenario, cognitive/secondary users share the same frequency band with licensed/primary users to efficiently exploit the spectrum. They do so without degrading the performance of the incumbent users, and may possibly even aid in transmitting their messages as cognitive users are assumed to possess the message(s) of primary user(s) and possibly other cognitive user(s). The survey begins with a short overview of the two-user overlay cognitive interference channel. The evolution from two-user to three-user overlay cognitive interference channels is described next, followed by generalizations to multi-user (arbitrary number of users) cognitive networks. The rest of the paper considers $K$ -user cognitive interference channels with different message knowledge structures at the transmitters. Novel capacity inner and outer bounds are proposed. Channel conditions under which the bounds meet, thus, characterizing the information theoretic capacity of the channel, for both linear deterministic and Gaussian channel models, are derived. The results show that for certain channel conditions distributed cognition, or having a cumulative message knowledge structure at the nodes, may not be worth the overhead as (approximately) the same capacity can be achieved by having only one global cognitive user whose role is to manage all the interference in the network. This paper concludes with future research directions.

Diana Maamari

Papers

Coverage in mmWave Cellular Networks With Base Station Co-Operation

Coverage in mmWave Cellular Networks with Base station Cooperation

Method and Apparatus for Multiuser MIMO Beamforming Training

The capacity of the ergodic miso channel with per-antenna power constraint and an application to the fading cognitive interference channel

Multi-user Cognitive Interference Channels: A Survey and New Capacity Results