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Enabling UAV Cellular with Millimeter-Wave Communication: Potentials and Approaches

Zhenyu Xiao, Pengfei Xia, Xiang-Gen Xia

arXiv:1602.03654v1cs.IT

TL;DR

The paper addresses how to support high-data-rate urgent or ad hoc communications with mmWave UAV cellular networks despite mobility-related challenges. It investigates hierarchical beamforming codebooks, Doppler, SDMA, blockage handling, and positioning-linked user discovery, finding promising benefits from directional transmission and SDMA while identifying adaptive cruising as necessary against blockage.

  • Problem

    MmWave UAV cellular networks must support high-data-rate urgent or ad hoc communications while addressing challenges associated with UAV movement, blockage, beamforming, access, and user discovery.

  • Method

    The paper investigates hierarchical beamforming codebooks, Doppler under UAV movement, mmWave SDMA, blockage-handling approaches, and the interaction between UAV positioning and directional user discovery.

  • Results

    The BMW-SS codebook generates different-width beams with excellent beam-detection performance and scales to very large antenna arrays; dominant multipath components vary slowly under high-gain directional transmission.

  • Takeaways & Limitations

    MmWave UAV cellular networks offer promising approaches for fast beamforming, increased capacity through SDMA, and mobility-based responses to blockage, while positioning and user discovery must be considered together.

Abstract

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To support high data rate urgent or ad hoc communications, we consider mmWave UAV cellular networks and the associated challenges and solutions. To enable fast beamforming training and tracking, we first investigate a hierarchical structure of beamforming codebooks and design of hierarchical codebooks with different beam widths via the sub-array techniques. We next examine the Doppler effect as a result of UAV movement and find that the Doppler effect may not be catastrophic when high gain directional transmission is used. We further explore the use of millimeter wave spatial division multiple access and demonstrate its clear advantage in improving the cellular network capacity. We also explore different ways of dealing with signal blockage and point out that possible adaptive UAV cruising algorithms would be necessary to counteract signal blockage. Finally, we identify a close relationship between UAV positioning and directional millimeter wave user discovery, where update of the former may directly impact the latter and vice versa.

I. INTRODUCTION

The paper studies mmWave UAV cellular networks for high-data-rate urgent or ad hoc communications, focusing on challenges intensified or created by UAV mobility and possible solutions.

  • Motivation: MmWave UAV cellular links are motivated by the need to rapidly support high-data-rate communications for ground and low-altitude users.The mmWave band offers abundant frequency-spectrum resources for traffic such as large video-monitoring data.
  • Challenges: UAV mobility intensifies conventional mmWave challenges, including directional communication, rapid channel variation, multi-user access, and blockage.Movement makes efficient beamforming training and tracking more important and requires extra consideration of Doppler effects.
  • Challenges: UAV position and directional user discovery are intertwined: a fixed position limits discovery to nearby users, while serving all potential users informs positioning.
  • Possible solutions: UAV mobility may alleviate blockage because intelligent cruising could move the UAV out of a blockage zone and establish line-of-sight communications.Blockage is identified as a significant performance-limiting factor in regular mmWave cellular networks.
  • Paper scope: The paper investigates channel propagation, hierarchical codebooks for fast beamforming training and tracking, mmWave SDMA, blockage handling, and positioning–user-discovery interaction.

II. CHANNEL PROPAGATION CHARACTERISTICS

The section examines mmWave propagation and transceiver design for UAV cellular systems, emphasizing directional beamforming, sparse channels, and mobility-related channel behavior.

  • Link budget: High-gain directional transmission can prevent higher carrier frequency from causing increased propagation loss when antenna area remains fixed.With directional antennas at both ends, received mmWave power could even exceed that of low-frequency signals.
  • Transceiver design: MmWave UAV systems generally use analog beamforming or hybrid precoding because full digital beamforming is difficult with many antennas and costly RF chains.These structures support one or more streams while using far fewer RF chains than antennas.
  • Channel structure: MmWave channels have few multipath components, making angles of departure and arrival sparse and enabling compressive-sensing-based channel estimation.Airborne environments may have less reflection and fewer angle-domain clusters than ground-user environments.
  • Channel model: A time-varying wideband channel model represents changing path coefficients, path delays, and steering angles across multipath components.The model includes the number of multipath components, pulse shape, relative delays, angles, and antenna-dependent steering vectors.
  • Mobility effects: Spatial beamforming along strong multipath components can mitigate delay spread, while high-gain directional transmission can make dominant components vary slowly despite UAV Doppler.The channel may therefore be treated as quasi-static for simplicity under suitable conditions.

III. FAST BEAMFORMING TRAINING AND TRACKING

Because UAV movement tightens the beamforming-training time constraint, the section considers beam steering and fast training and tracking for moving mmWave links.

  • Beamforming requirement: Beamforming must steer along strong multipath components at both the BS and MS to provide the necessary transmit and receive antenna gains.
  • Mobility constraint: UAV movement makes the beamforming-training time constraint more stringent than in conventional mmWave communications with static stations.The section therefore focuses on challenges and promising solutions for fast training and tracking.

A. Hierarchical Beam Search and Codebook Design

The paper uses hierarchical beam search and sub-array-based codebooks to reduce training complexity while forming wide beams without sacrificing total transmit power. BMW-SS avoids deep sinks and achieves the best reported success-rate performance among the compared approaches.

  • Codebook hierarchy: Hierarchical beam search organizes codewords into layers with progressively narrower beams, enabling search over the angle domain through a tree structure.Each layer contains M^k codewords with different steering angles that collectively cover the search space; a parent beam approximately covers its M child beams.
  • Training complexity: The fully hierarchical scheme has significantly lower complexity than exhaustive search, reducing the required beam-training time slots.Exhaustive search sequentially tests all beam-direction combinations, whereas the hierarchical scheme searches through the codebook layers.
  • Codebook limitations: Small RF-chain counts can produce deep sinks in sparse or hybrid codebooks, degrading wide-beam training performance.The problem becomes more severe as the number of RF chains decreases, while DEACT also suffers from small transmit power because few antennas remain active.
  • BMW-SS codebook design: BMW-SS forms very wide beams with a single RF chain without deactivating antennas or sacrificing total transmit power.The sub-array technique addresses the constant-amplitude constraint while retaining the active-antenna power advantage over DEACT.
  • Detection performance: BMW-SS achieves the best success-rate performance; BMW-SS and DEACT reach 100% at high SNR, whereas sparse codebooks do not.Success rate is defined as successfully acquiring the LOS component during beam search, and BMW-SS gains SNR from its larger number of active antennas.

B. Channel Variation and Beam Tracking

The paper argues that UAV motion does not automatically make mmWave channels unusable: narrow directional beams reduce angular dispersion and can slow individually resolvable multipath variation. Mobility information and hierarchical codebooks can further reduce beam-training and tracking overhead.

  • Doppler assessment: In mmWave links, Doppler spread depends on carrier frequency, mobile velocity, and total angular dispersion, so conventional calculations omit a relevant factor.The paper notes that directional transmission changes the angular-dispersion term considered in Doppler behavior.
  • Directional-channel behavior: Narrow directional transmission reduces multipath angular spread, so individually resolvable multipath components vary slowly even when the overall channel variation is large.The paper also relates narrower beams to a much larger realistic channel coherence time.
  • Tracking assistance: Prior information about steering-angle ranges, user location, and UAV movement can reduce beamforming training and tracking overhead.The relevant movement information includes GPS location, direction, and speed.
  • Tracking assistance: The hierarchical codebook supports fast tracking by using neighboring beam directions as short-list candidates after an acquired beam is identified.If w(S, i) is acquired, w(S, i −1) and w(S, i + 1) can serve as candidate directions.
  • Open scope: Important topics remain open for UAV mmWave beamforming training and tracking.The passage frames the area as requiring further investigation rather than presenting a complete solution.

IV. MMWAVE SPATIAL DIVISION MULTIPLE ACCESS

mmWave SDMA groups users by angular direction so spatially separated users can share the channel, while beamforming and multi-user detection manage their effective uplink channel. The paper reports that this approach exploits spatial degrees of freedom and provides higher multi-user capacity than low-frequency UAV cellular.

  • Performance: SDMA can boost overall multi-user capacity by up to NRF times when the base station has NRF transceiver RF chains.This reflects simultaneous service of users separated through spatial beams.
  • User grouping: Users are grouped by angle-of-departure clusters, and only users from different spatial groups access the channel simultaneously.The grouping is dynamic because UAVs and ground users may move.
  • Beamforming and detection: For U users, selected BS and MS codeword pairs form an effective U × U uplink channel processed with MMSE detection and successive interference cancellation.The uplink formulation assumes U ≤ NRF users and uses per-user power constraints.
  • Performance: The mmWave SDMA performance curve has almost the same slope as the perfect-interference-free bound rate.The remaining performance loss relative to the bound is mainly attributed to SNR loss.
  • Performance: The mmWave UAV cellular provides significantly higher multi-user capacity than the low-frequency UAV cellular, mainly through wider bandwidth and more SDMA users.The comparison uses the capacity expressions CMM = UBMM log2(1 + ρMM/U) and CLF = E{4BLF log2(1 + ρLF|h|2/4)}.

V. BLOCKAGE

Blockage can strongly degrade mmWave links when obstacles remove the LOS path, but UAV air-to-ground links are less severely affected than ground-to-ground links. The paper considers reflected-path beams, repositioning, and multiple UAVs as mitigation options.

  • Challenge: NLOS blockage by obstacles such as human bodies and buildings is a major source of mmWave performance degradation.The LOS path may be blocked while reflected paths remain available.
  • NLOS operation: Around 200 meter coverage is achievable for mmWave communications in NLOS environments despite blockage effects.Adaptive beam training can track strong first- and second-order reflection paths.
  • Mitigation: Candidate beams from first- and second-order reflection paths can be retained and pursued after the LOS path is lost.Using NLOS paths typically requires a lower modulation and coding scheme rate.
  • UAV advantage: UAV air-to-ground blockage is less severe than regular ground mmWave blockage because UAV elevation reduces reflection loss and generally increases LOS probability.The paper attributes the difference to the UAV’s high position relative to ground transmitters and receivers.
  • Mitigation: Adaptive cruising can move a UAV to restore LOS, while multiple UAVs provide alternative LOS links when one UAV is blocked.These approaches exploit UAV mobility and deployment flexibility.

VI. USER DISCOVERY

Directional transmissions make conventional user discovery difficult because UAVs and mobile stations may not hear one another’s access or broadcast signals. The paper proposes directional scanning and links discovery updates to UAV repositioning, with signaling overhead and update efficiency as key constraints.

  • Discovery challenge: Directional transmission can prevent the UAV from hearing a mobile station’s random-access preamble or the mobile station from hearing UAV broadcast signals.This limits direct application of conventional user discovery procedures.
  • Directional discovery: The UAV can transmit directional broadcast signals over multiple time slots to mimic an omnidirectional pattern.A PBCH-scanning mobile station can then detect at least one directional broadcast and send a preamble in a suitable slot.
  • Access procedure: Directional random-access responses can include timing advance, uplink grants, and mobile-station antenna-sector information before RRC signaling proceeds.The mobile station may also transmit and receive directionally.
  • Overhead control: Multiple directional transmissions create non-negligible user-discovery overhead, so coarse beam training is recommended before finer training for payload transmission.Fine beam training is deferred until after discovery to control overhead.
  • Positioning interaction: Repositioning changes user directions and can reveal users previously outside the UAV’s range, making positioning and directional discovery an iterative process.Moving from point A to B requires updating the directions of already discovered users and may expose a new mobile station.
  • Positioning interaction: Repositioning decisions trade network-performance improvement against signaling costs, and efficient direction updates remain undefined.The same tradeoff recurs when considering movement toward a centroid serving multiple users.

VII. CONCLUSIONS

The paper examines mmWave UAV cellular networks for urgent or ad hoc high-data-rate communications, addressing beamforming, Doppler, SDMA, blockage, and positioning–user-discovery challenges. It investigates hierarchical codebooks, directional transmission, spatial grouping, adaptive cruising, and the interaction between UAV positioning and directional user discovery.

  • Hierarchical Beamforming: Hierarchical beamforming codebooks support fast training and tracking, while BMW-SS generates different-width beams with excellent detection performance and scales to very large antenna arrays.The design uses beams on different layers and is implemented through sub-array techniques.
  • Doppler Effect: High-gain directional transmission limits the practical impact of UAV-movement Doppler because major multipath components vary slowly.The overall channel may experience fast Doppler, but the major multipath components undergo only slow variation.
  • Millimeter-Wave SDMA: Directional user grouping enables mmWave SDMA, allowing users from different spatial groups to access the base station simultaneously and improving capacity through bandwidth and spatial-domain reuse.The paper identifies significant capacity improvement, mainly due to large signal bandwidth and SDMA.
  • Signal Blockage: Blockage can be alleviated by UAV movement, but intelligent cruising algorithms are needed to fly out of blockage zones and reestablish line-of-sight links.The proposed direction is adaptive UAV cruising to counteract signal blockage.
  • Positioning and User Discovery: UAV positioning and directional mmWave user discovery are mutually coupled: fixed positioning limits discovery to nearby users, while serving all potential users affects positioning optimization.The paper studies this relationship as an iterative process for improving network performance.

BIOGRAPHIES

The biographies describe researchers whose affiliations, research interests, professional service, publications, and awards span wireless, signal-processing, coding, and millimeter-wave communications.

  • Research Interests: Research interests include millimeter-wave communications, UAV networks, wireless communications, networks, signal processing, MIMO, OFDM, and SAR/ISAR imaging.The listed interests also include space-time coding and digital signal processing.
  • Affiliations: The biographies identify affiliations with the University of Delaware in Newark, Delaware, and Shanghai, China.One biography names the Charles Black Evans Professor in Electrical and Computer Engineering.
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