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Wireless Communications in the Era of Big Data

Suzhi Bi, Rui Zhang, Zhi Ding, Shuguang Cui

arXiv:1508.06369v1cs.NI

TL;DR

Wireless bigdata traffic is outpacing improvements in computing capabilities and fronthaul/backhaul link rates, creating challenges for scalable wireless-system design. The article reviews signal-processing methods and networking structures for managing this traffic, while also examining ways to exploit it; one considered processing network achieves 301 Mbps common throughput, 43% and 31% higher than two alternatives.

  • Problem

    Rapidly growing mobile data volume surpasses the improvement pace of computing capabilities and fronthaul/backhaul link rates.

  • Method

    The article reviews state-of-the-art signal-processing methods and networking structures, including a hybrid signal-processing paradigm and approaches for bigdata features and processing complexity.

  • Results

    301 Mbps common throughput was achieved by the processing network, 43% and 31% higher than the BS-centric and cloud-centric networks, respectively.

  • Takeaways & Limitations

    The article identifies methods that may manage and take advantage of wireless bigdata traffic and outlines major obstacles for wireless communications in the mobile bigdata era.

Abstract

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The rapidly growing wave of wireless data service is pushing against the boundary of our communication network's processing power. The pervasive and exponentially increasing data traffic present imminent challenges to all the aspects of the wireless system design, such as spectrum efficiency, computing capabilities and fronthaul/backhaul link capacity. In this article, we discuss the challenges and opportunities in the design of scalable wireless systems to embrace such a "bigdata" era. On one hand, we review the state-of-the-art networking architectures and signal processing techniques adaptable for managing the bigdata traffic in wireless networks. On the other hand, instead of viewing mobile bigdata as a unwanted burden, we introduce methods to capitalize from the vast data traffic, for building a bigdata-aware wireless network with better wireless service quality and new mobile applications. We highlight several promising future research directions for wireless communications in the mobile bigdata era.

I. INTRODUCTION

Wireless bigdata strains processing capabilities and fronthaul/backhaul growth, motivating scalable architectures and efficient signal processing. The article also treats traffic characteristics as opportunities for data-aware wireless services.

  • Challenges: Mobile data growth and traffic amplification from MIMO and cooperating access points can overwhelm computing units and fronthaul links.Cooperating access points may generate multiple Gbps from one user’s fronthaul links for joint baseband processing.
  • Challenges: Current computing improvements and fronthaul/backhaul rate increases lag the growth of mobile traffic, requiring scalable wireless systems.The article frames scalable architecture and efficient signal processing as responses to this mismatch.
  • Opportunities: Mobile traffic exhibits mobility, spatial, temporal, and social correlations that can be extracted and exploited for wireless performance gains.The article gives caching popular content at wireless hotspots and data-driven network control as examples.
  • Article scope: The article asks how to build scalable architectures for bigdata traffic and how to use bigdata awareness to improve wireless performance.These questions organize the discussion of scalable processing and bigdata-aware networking.
  • Article scope: For scalability, the article introduces hybrid processing across BS/AP and CU levels plus traffic-management techniques balancing performance and complexity.For bigdata awareness, it discusses feature analytics, signal processing, networking structures, and future research directions.
  • Complementarity: Scalable network structure and bigdata awareness address different layers but can complement each other through combined long-term caching and real-time cache-assisted processing.The article places network/application-layer provisioning alongside physical-layer signal processing.

II. SCALABLE WIRELESS BIGDATA TRAFFIC MANAGEMENT

The paper motivates hybrid wireless architectures that distribute processing between base stations and a central unit to manage growing traffic and fronthaul constraints. The proposed structure combines local and central processing, learning units, and caches, while fronthaul technology determines feasible traffic-management methods.

  • A. A hybrid network structure: Existing BS-centric systems centralize radio access, baseband processing, and radio-resource control at base stations, while cloud-centric designs move computation to the cloud.Cloud-centric processing can mitigate inter-cell interference and reduce unit-cell deployment cost, but centralized traffic can exceed fronthaul capacity.
  • A. A hybrid network structure: High traffic can overwhelm fronthaul links and system computing units, motivating scalable architectures and signal-processing methods.Smaller cells improve frequency reuse but increase inter-cell interference and densely deployed base-station costs.
  • A. A hybrid network structure: The hybrid structure adaptively selects BS-level, CU-level, or parallel processing according to channel conditions and data-content correlations.It retains a C-RAN skeleton while adding programmable BS-level processing modules.
  • A. A hybrid network structure: Hybrid-network BPUs support message encoding and decoding at both BSs and the CU, while learning units analyze traffic and caches reduce repeated fronthaul transmissions.Learning units are installed at BSs and CUs, and caches store popular content to save fronthaul bandwidth.
  • A. A hybrid network structure: Digital fronthaul supports compression and opportunistic decoding, whereas RoF is simpler and less expensive but is more susceptible to noise, distortion, and synchronization difficulty.The paper therefore focuses on digital-fronthaul traffic-management methods.

B. Hybrid signal processing models

Fronthaul links impose throughput limits that constrain wireless-system optimization. The paper introduces scalable fronthaul data-management techniques for the hybrid network structure.

  • B. Hybrid signal processing models: A wireless or fiber-optic fronthaul link has finite throughput, and transmission beyond capacity can cause signal distortion and poor decoding.A commercial single-optical-carrier digital link normally operates at roughly 10 Gbps.
  • B. Hybrid signal processing models: System performance must therefore be optimized under fronthaul link-capacity constraints.The paper applies this constraint to the hybrid network structure.
  • B. Hybrid signal processing models: The paper organizes scalable fronthaul data management into three categories: partial cooperation, distributed encoding/decoding, and data compression.These categories target traffic reduction while retaining useful cooperative processing.

1) Data compression:

The paper reviews compression, cooperation, and distributed processing methods for managing fronthaul traffic in hybrid networks. A numerical example reports that hybrid processing achieves the highest common-throughput among the three considered architectures.

  • 1) Data compression:: Tighter fronthaul constraints require coarser compression with larger compression noise, reflecting the trade-off between capacity usage and signal fidelity.The Gaussian test-channel model represents compression as additive noise and links required transmission rate to mutual information.
  • 1) Data compression:: Distributed Wyner-Ziv compression exploits signal correlation across BSs and yields significant capacity gains over independent quantization, especially at low backhaul capacity.The compression design sets cross-BS compression-noise covariance while maximizing information rate under fronthaul constraints.
  • 1) Data compression:: Independent BS-level compression reduces the complexity of joint codebook determination and decompression, while uniform scalar quantization lowers implementation cost.Uniform scalar quantization performs closely to the Gaussian test-channel model, indicating efficient fronthaul use with simple quantizers.
  • 1) Data compression:: Partial cooperation limits cooperating elements, while distributed decoding locally decodes messages and forwards less quantized signal information to the CU.Opportunistic hybrid decoding selects local BS decoding at sufficiently high SNR and CU joint decoding otherwise; local decisions can cancel interference and reduce fronthaul traffic.

3) Cache-assisted processing:

Cache-assisted processing uses traffic characteristics and distributed caches to reduce fronthaul/backhaul pressure while improving wireless service performance. The section illustrates these gains through cached delivery, cooperation, and cloudlet-based local processing, while noting open cross-layer optimization challenges.

  • Downlink cache-assisted processing: 2 units per second is achieved for MT1 through cached delivery from BS2, compared with 1 unit per second via congested BS1.BS2 avoids the congestion constraint on the CU-to-BS2 link.
  • Cooperative transmission: Cooperating base stations can serve users jointly, with MT2 achieving a higher data rate of 3 units per second.The example uses cooperation between BS2 and BS3.
  • Cache-assisted resource allocation: Cache-assisted resource allocation integrates cache status with wireless resource decisions to save bandwidth as overlapping content requests increase.The approach spans cache placement, wireless interference, routing, and node selection.
  • Open challenges: System-wide cache-assisted optimization remains open because cache placement, interference, routing, and combinatorial decisions are interleaved.The section identifies comprehensive design tradeoffs as a future-study topic.

B. Bigdata analytical tools

Bigdata analytical tools extract traffic characteristics from wireless data using learning units at base stations and central units. The section organizes these tools around stochastic modeling, data mining, and machine learning.

  • Analytical architecture: Learning units installed at base stations and central units acquire, analyze, and exploit mobile traffic characteristics through embedded data-analytical algorithms.These units support wireless traffic analysis and related applications.
  • Stochastic modeling: Stochastic modeling uses probabilistic models such as Markov, hidden Markov, geometric, and time-series models to capture traffic features and dynamics.Collected user data can estimate model parameters, including a Markov chain's transition probability matrix.
  • Data mining: Data mining extracts implicit structures by finding frequent trajectory segments, matching current movement to mobility profiles, and clustering behavioral patterns.These methods support mobility prediction and context-aware mobile computing.

3) Machine learning:

Machine learning links wireless data to actions for automated processing, while large-scale analytics must address high volume, dimensionality, uneven quality, and incomplete data. The section therefore combines learning methods with distributed optimization and dimensionality reduction, then connects extracted characteristics to cache, cloud, crowd, and context-aware services.

  • Machine learning: Machine learning establishes functional relationships between input data and output actions, enabling automated processing of unseen patterns.Classification and regression support context identification, traffic prediction, and fitting of mobility-related distributions.
  • Reinforcement learning: Reinforcement learning can select real-time handoff and admission-control actions from traffic-load states and incoming requests to maximize long-term rewards.Rewards may reflect reductions in dropped calls or failed connections.
  • Bigdata challenges: Wireless bigdata challenges conventional analytics through high volume, large dimensionality, uneven data quality, and incomplete or complex datasets.The section motivates advanced learning and complexity-reduction methods for these conditions.
  • Complexity reduction: Distributed optimization decomposes large-scale statistical learning into parallel subproblems, relieving central-unit computation and fronthaul/backhaul bandwidth pressure.Examples include primal/dual decomposition and ADMM.
  • Complexity reduction: Dimension reduction and tensor decomposition reduce processed data volume or storage requirements while retaining key features of mobile bigdata.Tensor methods represent high-order arrays with low-order components, reducing storage hardware requirements.

IV. FUTURE RESEARCH DIRECTIONS

The paper identifies multiple open research problems in wireless system design during the mobile bigdata era. It highlights research opportunities whose applications and impact remain insufficiently studied.

  • Research agenda: Wireless system design in the mobile bigdata era contains research problems with important applications and impact that remain to be studied.The section frames these as opportunities beyond the topics already discussed.
  • Research agenda: The future agenda extends beyond the previously discussed issues to additional wireless bigdata research topics.The section explicitly distinguishes the highlighted topics from the broader set of open problems.
  • Research agenda: The paper highlights several research topics it considers particularly promising and exciting.These topics are presented as future research directions.

A. Reduced-complexity fronthaul processing

Frontal and CU processing in wireless bigdata systems faces scalability barriers from many constraints, non-convex optimization, and combinatorial coordination choices. The section points toward practical complexity-reduction and caching-oriented designs.

  • Frontal processing: Real-time optimal compression-noise covariance calculation is impeded by many fronthaul constraints and non-convex problem structure.The difficulty extends to generating practical joint compression codebooks from the resulting covariance matrix.
  • Frontal processing: Sub-optimal practical schemes such as scalar quantization merit greater consideration for fronthaul-constrained compression design.
  • CU processing: CU-level encoding and decoding becomes computationally difficult for large-scale multi-user detection and limited-cooperation decisions.The associated choices include antennas, relays, modulation and coding combinations, and base-station selections.
  • CU processing: Practical complexity-reduction algorithms are needed for combinatorial limited-cooperation problems and large-scale CU processing.
  • Cache-assisted processing: BS-level caching is simple, low-cost, and naturally integrated with bigdata analytical tools, but cache-assisted allocation research remains at an early stage.Open needs include theoretical capacity-gain analysis, practical optimization frameworks, and integration of multiple bigdata characteristics.

C. Distributed network traffic control

Distributed or mixed centralized–decentralized control is proposed for scalable wireless bigdata networking, while data storage and processing must also address security and privacy. These directions remain constrained by coupled problems and incomplete traffic knowledge.

  • Distributed control: Distributed control and computing can reduce CU complexity, backhaul traffic, and single-point-failure risk without compromising overall system performance.
  • Distributed control: SDN programmability supports distributed control mechanisms with greater flexibility and lower cost.
  • Distributed control: A mixed centralized and decentralized control framework is a promising direction for wireless networks supporting mobile bigdata.Its feasibility and complexity reduction are constrained by coupling constraints in the backhaul and partial traffic knowledge.
  • Security and privacy: Wireless bigdata storage and processing raise security and privacy concerns, motivating secure yet efficient processing and storage methods.The paper identifies authenticated access, data integrity, and confidentiality during storage and processing as requirements.
  • Research directions: The article reviews signal-processing methods and networking structures for managing and potentially exploiting wireless bigdata traffic, then identifies future research problems.
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